feature-engineering-techiques-on-house-prices.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":7876,"sourceType":"datasetVersion","datasetId":5227}],"dockerImageVersionId":30673,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-03-31T14:59:54.469710Z","iopub.execute_input":"2024-03-31T14:59:54.470108Z","iopub.status.idle":"2024-03-31T14:59:55.558015Z","shell.execute_reply.started":"2024-03-31T14:59:54.470076Z","shell.execute_reply":"2024-03-31T14:59:55.556627Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/california-housing-prices/housing.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# FEATURE ENGINEERING :\n## * **Feature engineering** plays a crucial role in transforming raw data into a format that is more suitable for machine learning models.\n## * It helps **handle complex relationships, missing data, and non-normality**, while also **reducing dimensionality** and enhancing interpretability, ultimately making the model more effective in learning from the data.","metadata":{}},{"cell_type":"markdown","source":"## NOTE :\n### The purpose of this notebook is to **learn different feature engineering techniques available** and not to find the best model for the dataset.\n\n#### Dataset Link - https://www.kaggle.com/datasets/camnugent/california-housing-prices","metadata":{}},{"cell_type":"markdown","source":"The feature engineering techniques discussed below are -","metadata":{}},{"cell_type":"markdown","source":"### 1. DIMENSIONALITY REDUCTION(PCA)\n### 2. SCALING (STANDARDSCALER, NORMALIZER, MINMAX)\n### 3. CATEGORICAL ENCODING (DUMMY/ONE-HOT)\n### 4. BINNING (GROUPING/AGGREGATING)\n### 5. CLUSTERING\n### 6. FEATURE SELECTION(COMBINATION)","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.559853Z","iopub.execute_input":"2024-03-31T14:59:55.560824Z","iopub.status.idle":"2024-03-31T14:59:55.565789Z","shell.execute_reply.started":"2024-03-31T14:59:55.560788Z","shell.execute_reply":"2024-03-31T14:59:55.564647Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/california-housing-prices/housing.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.567235Z","iopub.execute_input":"2024-03-31T14:59:55.568354Z","iopub.status.idle":"2024-03-31T14:59:55.628781Z","shell.execute_reply.started":"2024-03-31T14:59:55.568224Z","shell.execute_reply":"2024-03-31T14:59:55.627075Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.631429Z","iopub.execute_input":"2024-03-31T14:59:55.632099Z","iopub.status.idle":"2024-03-31T14:59:55.670911Z","shell.execute_reply.started":"2024-03-31T14:59:55.632065Z","shell.execute_reply":"2024-03-31T14:59:55.669368Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -122.23     37.88                41.0        880.0           129.0   \n1        -122.22     37.86                21.0       7099.0          1106.0   \n2        -122.24     37.85                52.0       1467.0           190.0   \n3        -122.25     37.85                52.0       1274.0           235.0   \n4        -122.25     37.85                52.0       1627.0           280.0   \n...          ...       ...                 ...          ...             ...   \n20635    -121.09     39.48                25.0       1665.0           374.0   \n20636    -121.21     39.49                18.0        697.0           150.0   \n20637    -121.22     39.43                17.0       2254.0           485.0   \n20638    -121.32     39.43                18.0       1860.0           409.0   \n20639    -121.24     39.37                16.0       2785.0           616.0   \n\n       population  households  median_income  median_house_value  \\\n0           322.0       126.0         8.3252            452600.0   \n1          2401.0      1138.0         8.3014            358500.0   \n2           496.0       177.0         7.2574            352100.0   \n3           558.0       219.0         5.6431            341300.0   \n4           565.0       259.0         3.8462            342200.0   \n...           ...         ...            ...                 ...   \n20635       845.0       330.0         1.5603             78100.0   \n20636       356.0       114.0         2.5568             77100.0   \n20637      1007.0       433.0         1.7000             92300.0   \n20638       741.0       349.0         1.8672             84700.0   \n20639      1387.0       530.0         2.3886             89400.0   \n\n      ocean_proximity  \n0            NEAR BAY  \n1            NEAR BAY  \n2            NEAR BAY  \n3            NEAR BAY  \n4            NEAR BAY  \n...               ...  \n20635          INLAND  \n20636          INLAND  \n20637          INLAND  \n20638          INLAND  \n20639          INLAND  \n\n[20640 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1627.0</td>\n      <td>280.0</td>\n      <td>565.0</td>\n      <td>259.0</td>\n      <td>3.8462</td>\n      <td>342200.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>20635</th>\n      <td>-121.09</td>\n      <td>39.48</td>\n      <td>25.0</td>\n      <td>1665.0</td>\n      <td>374.0</td>\n      <td>845.0</td>\n      <td>330.0</td>\n      <td>1.5603</td>\n      <td>78100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20636</th>\n      <td>-121.21</td>\n      <td>39.49</td>\n      <td>18.0</td>\n      <td>697.0</td>\n      <td>150.0</td>\n      <td>356.0</td>\n      <td>114.0</td>\n      <td>2.5568</td>\n      <td>77100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20637</th>\n      <td>-121.22</td>\n      <td>39.43</td>\n      <td>17.0</td>\n      <td>2254.0</td>\n      <td>485.0</td>\n      <td>1007.0</td>\n      <td>433.0</td>\n      <td>1.7000</td>\n      <td>92300.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20638</th>\n      <td>-121.32</td>\n      <td>39.43</td>\n      <td>18.0</td>\n      <td>1860.0</td>\n      <td>409.0</td>\n      <td>741.0</td>\n      <td>349.0</td>\n      <td>1.8672</td>\n      <td>84700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20639</th>\n      <td>-121.24</td>\n      <td>39.37</td>\n      <td>16.0</td>\n      <td>2785.0</td>\n      <td>616.0</td>\n      <td>1387.0</td>\n      <td>530.0</td>\n      <td>2.3886</td>\n      <td>89400.0</td>\n      <td>INLAND</td>\n    </tr>\n  </tbody>\n</table>\n<p>20640 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.672407Z","iopub.execute_input":"2024-03-31T14:59:55.672868Z","iopub.status.idle":"2024-03-31T14:59:55.683995Z","shell.execute_reply.started":"2024-03-31T14:59:55.672826Z","shell.execute_reply":"2024-03-31T14:59:55.682422Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"longitude               0\nlatitude                0\nhousing_median_age      0\ntotal_rooms             0\ntotal_bedrooms        207\npopulation              0\nhouseholds              0\nmedian_income           0\nmedian_house_value      0\nocean_proximity         0\ndtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"### It seems 207 rows are missing bedroom count. \n### It would be prudent to drop these rows as they wont provide significant value with missing bedroom count.","metadata":{}},{"cell_type":"code","source":"df = df.dropna()\ndf","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.685449Z","iopub.execute_input":"2024-03-31T14:59:55.685944Z","iopub.status.idle":"2024-03-31T14:59:55.714563Z","shell.execute_reply.started":"2024-03-31T14:59:55.685907Z","shell.execute_reply":"2024-03-31T14:59:55.713375Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -122.23     37.88                41.0        880.0           129.0   \n1        -122.22     37.86                21.0       7099.0          1106.0   \n2        -122.24     37.85                52.0       1467.0           190.0   \n3        -122.25     37.85                52.0       1274.0           235.0   \n4        -122.25     37.85                52.0       1627.0           280.0   \n...          ...       ...                 ...          ...             ...   \n20635    -121.09     39.48                25.0       1665.0           374.0   \n20636    -121.21     39.49                18.0        697.0           150.0   \n20637    -121.22     39.43                17.0       2254.0           485.0   \n20638    -121.32     39.43                18.0       1860.0           409.0   \n20639    -121.24     39.37                16.0       2785.0           616.0   \n\n       population  households  median_income  median_house_value  \\\n0           322.0       126.0         8.3252            452600.0   \n1          2401.0      1138.0         8.3014            358500.0   \n2           496.0       177.0         7.2574            352100.0   \n3           558.0       219.0         5.6431            341300.0   \n4           565.0       259.0         3.8462            342200.0   \n...           ...         ...            ...                 ...   \n20635       845.0       330.0         1.5603             78100.0   \n20636       356.0       114.0         2.5568             77100.0   \n20637      1007.0       433.0         1.7000             92300.0   \n20638       741.0       349.0         1.8672             84700.0   \n20639      1387.0       530.0         2.3886             89400.0   \n\n      ocean_proximity  \n0            NEAR BAY  \n1            NEAR BAY  \n2            NEAR BAY  \n3            NEAR BAY  \n4            NEAR BAY  \n...               ...  \n20635          INLAND  \n20636          INLAND  \n20637          INLAND  \n20638          INLAND  \n20639          INLAND  \n\n[20433 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-122.23</td>\n      <td>37.88</td>\n      <td>41.0</td>\n      <td>880.0</td>\n      <td>129.0</td>\n      <td>322.0</td>\n      <td>126.0</td>\n      <td>8.3252</td>\n      <td>452600.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-122.22</td>\n      <td>37.86</td>\n      <td>21.0</td>\n      <td>7099.0</td>\n      <td>1106.0</td>\n      <td>2401.0</td>\n      <td>1138.0</td>\n      <td>8.3014</td>\n      <td>358500.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-122.24</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1467.0</td>\n      <td>190.0</td>\n      <td>496.0</td>\n      <td>177.0</td>\n      <td>7.2574</td>\n      <td>352100.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1274.0</td>\n      <td>235.0</td>\n      <td>558.0</td>\n      <td>219.0</td>\n      <td>5.6431</td>\n      <td>341300.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-122.25</td>\n      <td>37.85</td>\n      <td>52.0</td>\n      <td>1627.0</td>\n      <td>280.0</td>\n      <td>565.0</td>\n      <td>259.0</td>\n      <td>3.8462</td>\n      <td>342200.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>20635</th>\n      <td>-121.09</td>\n      <td>39.48</td>\n      <td>25.0</td>\n      <td>1665.0</td>\n      <td>374.0</td>\n      <td>845.0</td>\n      <td>330.0</td>\n      <td>1.5603</td>\n      <td>78100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20636</th>\n      <td>-121.21</td>\n      <td>39.49</td>\n      <td>18.0</td>\n      <td>697.0</td>\n      <td>150.0</td>\n      <td>356.0</td>\n      <td>114.0</td>\n      <td>2.5568</td>\n      <td>77100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20637</th>\n      <td>-121.22</td>\n      <td>39.43</td>\n      <td>17.0</td>\n      <td>2254.0</td>\n      <td>485.0</td>\n      <td>1007.0</td>\n      <td>433.0</td>\n      <td>1.7000</td>\n      <td>92300.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20638</th>\n      <td>-121.32</td>\n      <td>39.43</td>\n      <td>18.0</td>\n      <td>1860.0</td>\n      <td>409.0</td>\n      <td>741.0</td>\n      <td>349.0</td>\n      <td>1.8672</td>\n      <td>84700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>20639</th>\n      <td>-121.24</td>\n      <td>39.37</td>\n      <td>16.0</td>\n      <td>2785.0</td>\n      <td>616.0</td>\n      <td>1387.0</td>\n      <td>530.0</td>\n      <td>2.3886</td>\n      <td>89400.0</td>\n      <td>INLAND</td>\n    </tr>\n  </tbody>\n</table>\n<p>20433 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.describe()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.715921Z","iopub.execute_input":"2024-03-31T14:59:55.716887Z","iopub.status.idle":"2024-03-31T14:59:55.765761Z","shell.execute_reply.started":"2024-03-31T14:59:55.716849Z","shell.execute_reply":"2024-03-31T14:59:55.764555Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"          longitude      latitude  housing_median_age   total_rooms  \\\ncount  20433.000000  20433.000000        20433.000000  20433.000000   \nmean    -119.570689     35.633221           28.633094   2636.504233   \nstd        2.003578      2.136348           12.591805   2185.269567   \nmin     -124.350000     32.540000            1.000000      2.000000   \n25%     -121.800000     33.930000           18.000000   1450.000000   \n50%     -118.490000     34.260000           29.000000   2127.000000   \n75%     -118.010000     37.720000           37.000000   3143.000000   \nmax     -114.310000     41.950000           52.000000  39320.000000   \n\n       total_bedrooms    population    households  median_income  \\\ncount    20433.000000  20433.000000  20433.000000   20433.000000   \nmean       537.870553   1424.946949    499.433465       3.871162   \nstd        421.385070   1133.208490    382.299226       1.899291   \nmin          1.000000      3.000000      1.000000       0.499900   \n25%        296.000000    787.000000    280.000000       2.563700   \n50%        435.000000   1166.000000    409.000000       3.536500   \n75%        647.000000   1722.000000    604.000000       4.744000   \nmax       6445.000000  35682.000000   6082.000000      15.000100   \n\n       median_house_value  \ncount        20433.000000  \nmean        206864.413155  \nstd         115435.667099  \nmin          14999.000000  \n25%         119500.000000  \n50%         179700.000000  \n75%         264700.000000  \nmax         500001.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n      <td>20433.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>-119.570689</td>\n      <td>35.633221</td>\n      <td>28.633094</td>\n      <td>2636.504233</td>\n      <td>537.870553</td>\n      <td>1424.946949</td>\n      <td>499.433465</td>\n      <td>3.871162</td>\n      <td>206864.413155</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.003578</td>\n      <td>2.136348</td>\n      <td>12.591805</td>\n      <td>2185.269567</td>\n      <td>421.385070</td>\n      <td>1133.208490</td>\n      <td>382.299226</td>\n      <td>1.899291</td>\n      <td>115435.667099</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-124.350000</td>\n      <td>32.540000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n      <td>0.499900</td>\n      <td>14999.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>-121.800000</td>\n      <td>33.930000</td>\n      <td>18.000000</td>\n      <td>1450.000000</td>\n      <td>296.000000</td>\n      <td>787.000000</td>\n      <td>280.000000</td>\n      <td>2.563700</td>\n      <td>119500.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>-118.490000</td>\n      <td>34.260000</td>\n      <td>29.000000</td>\n      <td>2127.000000</td>\n      <td>435.000000</td>\n      <td>1166.000000</td>\n      <td>409.000000</td>\n      <td>3.536500</td>\n      <td>179700.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>-118.010000</td>\n      <td>37.720000</td>\n      <td>37.000000</td>\n      <td>3143.000000</td>\n      <td>647.000000</td>\n      <td>1722.000000</td>\n      <td>604.000000</td>\n      <td>4.744000</td>\n      <td>264700.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>-114.310000</td>\n      <td>41.950000</td>\n      <td>52.000000</td>\n      <td>39320.000000</td>\n      <td>6445.000000</td>\n      <td>35682.000000</td>\n      <td>6082.000000</td>\n      <td>15.000100</td>\n      <td>500001.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df[df['total_rooms'] == 39320]","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.768963Z","iopub.execute_input":"2024-03-31T14:59:55.769386Z","iopub.status.idle":"2024-03-31T14:59:55.788945Z","shell.execute_reply.started":"2024-03-31T14:59:55.769354Z","shell.execute_reply":"2024-03-31T14:59:55.787437Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n13139    -121.44     38.43                 3.0      39320.0          6210.0   \n\n       population  households  median_income  median_house_value  \\\n13139     16305.0      5358.0         4.9516            153700.0   \n\n      ocean_proximity  \n13139          INLAND  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>13139</th>\n      <td>-121.44</td>\n      <td>38.43</td>\n      <td>3.0</td>\n      <td>39320.0</td>\n      <td>6210.0</td>\n      <td>16305.0</td>\n      <td>5358.0</td>\n      <td>4.9516</td>\n      <td>153700.0</td>\n      <td>INLAND</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"# SPLITING INTO TRAIN AND TEST DATA","metadata":{}},{"cell_type":"code","source":"#shuffling the rows\ndf=df.sample(frac=1)\ndf","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.792862Z","iopub.execute_input":"2024-03-31T14:59:55.793205Z","iopub.status.idle":"2024-03-31T14:59:55.821797Z","shell.execute_reply.started":"2024-03-31T14:59:55.793177Z","shell.execute_reply":"2024-03-31T14:59:55.820528Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n1237     -120.37     38.25                13.0       4495.0           856.0   \n12798    -121.45     38.62                38.0       2419.0           605.0   \n18408    -121.80     37.27                17.0       3912.0           737.0   \n1030     -120.25     38.55                15.0       4403.0           891.0   \n6038     -117.73     34.07                33.0       1921.0           489.0   \n...          ...       ...                 ...          ...             ...   \n5040     -118.32     33.98                44.0       1448.0           314.0   \n8712     -118.36     33.86                36.0        681.0           122.0   \n18668    -121.91     36.99                23.0       5675.0           964.0   \n16136    -122.49     37.79                52.0       3146.0           478.0   \n5532     -118.42     33.97                44.0       1462.0           240.0   \n\n       population  households  median_income  median_house_value  \\\n1237       1149.0       459.0         2.5352            113700.0   \n12798      1696.0       503.0         1.4861             63100.0   \n18408      2693.0       746.0         5.0772            221500.0   \n1030       1103.0       433.0         3.0125            111700.0   \n6038       1430.0       467.0         2.3406            122600.0   \n...           ...         ...            ...                 ...   \n5040        861.0       310.0         2.2396            108600.0   \n8712        360.0       128.0         5.2799            332600.0   \n18668      2197.0       880.0         4.8693            322300.0   \n16136      1143.0       455.0         6.1407            500001.0   \n5532        562.0       237.0         4.9375            365200.0   \n\n      ocean_proximity  \n1237           INLAND  \n12798          INLAND  \n18408       <1H OCEAN  \n1030           INLAND  \n6038           INLAND  \n...               ...  \n5040        <1H OCEAN  \n8712        <1H OCEAN  \n18668      NEAR OCEAN  \n16136        NEAR BAY  \n5532        <1H OCEAN  \n\n[20433 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1237</th>\n      <td>-120.37</td>\n      <td>38.25</td>\n      <td>13.0</td>\n      <td>4495.0</td>\n      <td>856.0</td>\n      <td>1149.0</td>\n      <td>459.0</td>\n      <td>2.5352</td>\n      <td>113700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>12798</th>\n      <td>-121.45</td>\n      <td>38.62</td>\n      <td>38.0</td>\n      <td>2419.0</td>\n      <td>605.0</td>\n      <td>1696.0</td>\n      <td>503.0</td>\n      <td>1.4861</td>\n      <td>63100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>18408</th>\n      <td>-121.80</td>\n      <td>37.27</td>\n      <td>17.0</td>\n      <td>3912.0</td>\n      <td>737.0</td>\n      <td>2693.0</td>\n      <td>746.0</td>\n      <td>5.0772</td>\n      <td>221500.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>1030</th>\n      <td>-120.25</td>\n      <td>38.55</td>\n      <td>15.0</td>\n      <td>4403.0</td>\n      <td>891.0</td>\n      <td>1103.0</td>\n      <td>433.0</td>\n      <td>3.0125</td>\n      <td>111700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>6038</th>\n      <td>-117.73</td>\n      <td>34.07</td>\n      <td>33.0</td>\n      <td>1921.0</td>\n      <td>489.0</td>\n      <td>1430.0</td>\n      <td>467.0</td>\n      <td>2.3406</td>\n      <td>122600.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>5040</th>\n      <td>-118.32</td>\n      <td>33.98</td>\n      <td>44.0</td>\n      <td>1448.0</td>\n      <td>314.0</td>\n      <td>861.0</td>\n      <td>310.0</td>\n      <td>2.2396</td>\n      <td>108600.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>8712</th>\n      <td>-118.36</td>\n      <td>33.86</td>\n      <td>36.0</td>\n      <td>681.0</td>\n      <td>122.0</td>\n      <td>360.0</td>\n      <td>128.0</td>\n      <td>5.2799</td>\n      <td>332600.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>18668</th>\n      <td>-121.91</td>\n      <td>36.99</td>\n      <td>23.0</td>\n      <td>5675.0</td>\n      <td>964.0</td>\n      <td>2197.0</td>\n      <td>880.0</td>\n      <td>4.8693</td>\n      <td>322300.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>16136</th>\n      <td>-122.49</td>\n      <td>37.79</td>\n      <td>52.0</td>\n      <td>3146.0</td>\n      <td>478.0</td>\n      <td>1143.0</td>\n      <td>455.0</td>\n      <td>6.1407</td>\n      <td>500001.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>5532</th>\n      <td>-118.42</td>\n      <td>33.97</td>\n      <td>44.0</td>\n      <td>1462.0</td>\n      <td>240.0</td>\n      <td>562.0</td>\n      <td>237.0</td>\n      <td>4.9375</td>\n      <td>365200.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n  </tbody>\n</table>\n<p>20433 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# Dividing the data into 80,20 for train and test respectively\n\ntrain = df[:17000] #first 17000 rows for training\ntrain = train.reset_index(drop=True) # reset the index so that its easier to understand\n\ntest = df[17000:]  #from 17000 till the end num of rows for training (would be 3433 values)\ntest = test.reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.825876Z","iopub.execute_input":"2024-03-31T14:59:55.826293Z","iopub.status.idle":"2024-03-31T14:59:55.834574Z","shell.execute_reply.started":"2024-03-31T14:59:55.826257Z","shell.execute_reply":"2024-03-31T14:59:55.833193Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"train","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.836632Z","iopub.execute_input":"2024-03-31T14:59:55.837125Z","iopub.status.idle":"2024-03-31T14:59:55.861396Z","shell.execute_reply.started":"2024-03-31T14:59:55.837003Z","shell.execute_reply":"2024-03-31T14:59:55.860153Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -120.37     38.25                13.0       4495.0           856.0   \n1        -121.45     38.62                38.0       2419.0           605.0   \n2        -121.80     37.27                17.0       3912.0           737.0   \n3        -120.25     38.55                15.0       4403.0           891.0   \n4        -117.73     34.07                33.0       1921.0           489.0   \n...          ...       ...                 ...          ...             ...   \n16995    -118.43     34.16                34.0       2459.0           489.0   \n16996    -117.07     32.77                38.0       3779.0           614.0   \n16997    -117.13     32.81                19.0       2157.0           554.0   \n16998    -119.51     34.39                32.0       1921.0           394.0   \n16999    -122.14     37.43                52.0       1327.0           190.0   \n\n       population  households  median_income  median_house_value  \\\n0          1149.0       459.0         2.5352            113700.0   \n1          1696.0       503.0         1.4861             63100.0   \n2          2693.0       746.0         5.0772            221500.0   \n3          1103.0       433.0         3.0125            111700.0   \n4          1430.0       467.0         2.3406            122600.0   \n...           ...         ...            ...                 ...   \n16995      1139.0       463.0         4.0347            353600.0   \n16996      1495.0       614.0         4.3529            184000.0   \n16997      1349.0       535.0         2.7652            177400.0   \n16998       951.0       334.0         3.2330            346000.0   \n16999       467.0       189.0        12.5902            500001.0   \n\n      ocean_proximity  \n0              INLAND  \n1              INLAND  \n2           <1H OCEAN  \n3              INLAND  \n4              INLAND  \n...               ...  \n16995       <1H OCEAN  \n16996      NEAR OCEAN  \n16997      NEAR OCEAN  \n16998      NEAR OCEAN  \n16999        NEAR BAY  \n\n[17000 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-120.37</td>\n      <td>38.25</td>\n      <td>13.0</td>\n      <td>4495.0</td>\n      <td>856.0</td>\n      <td>1149.0</td>\n      <td>459.0</td>\n      <td>2.5352</td>\n      <td>113700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-121.45</td>\n      <td>38.62</td>\n      <td>38.0</td>\n      <td>2419.0</td>\n      <td>605.0</td>\n      <td>1696.0</td>\n      <td>503.0</td>\n      <td>1.4861</td>\n      <td>63100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-121.80</td>\n      <td>37.27</td>\n      <td>17.0</td>\n      <td>3912.0</td>\n      <td>737.0</td>\n      <td>2693.0</td>\n      <td>746.0</td>\n      <td>5.0772</td>\n      <td>221500.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-120.25</td>\n      <td>38.55</td>\n      <td>15.0</td>\n      <td>4403.0</td>\n      <td>891.0</td>\n      <td>1103.0</td>\n      <td>433.0</td>\n      <td>3.0125</td>\n      <td>111700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-117.73</td>\n      <td>34.07</td>\n      <td>33.0</td>\n      <td>1921.0</td>\n      <td>489.0</td>\n      <td>1430.0</td>\n      <td>467.0</td>\n      <td>2.3406</td>\n      <td>122600.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>16995</th>\n      <td>-118.43</td>\n      <td>34.16</td>\n      <td>34.0</td>\n      <td>2459.0</td>\n      <td>489.0</td>\n      <td>1139.0</td>\n      <td>463.0</td>\n      <td>4.0347</td>\n      <td>353600.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>16996</th>\n      <td>-117.07</td>\n      <td>32.77</td>\n      <td>38.0</td>\n      <td>3779.0</td>\n      <td>614.0</td>\n      <td>1495.0</td>\n      <td>614.0</td>\n      <td>4.3529</td>\n      <td>184000.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>16997</th>\n      <td>-117.13</td>\n      <td>32.81</td>\n      <td>19.0</td>\n      <td>2157.0</td>\n      <td>554.0</td>\n      <td>1349.0</td>\n      <td>535.0</td>\n      <td>2.7652</td>\n      <td>177400.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>16998</th>\n      <td>-119.51</td>\n      <td>34.39</td>\n      <td>32.0</td>\n      <td>1921.0</td>\n      <td>394.0</td>\n      <td>951.0</td>\n      <td>334.0</td>\n      <td>3.2330</td>\n      <td>346000.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>16999</th>\n      <td>-122.14</td>\n      <td>37.43</td>\n      <td>52.0</td>\n      <td>1327.0</td>\n      <td>190.0</td>\n      <td>467.0</td>\n      <td>189.0</td>\n      <td>12.5902</td>\n      <td>500001.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n  </tbody>\n</table>\n<p>17000 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.862747Z","iopub.execute_input":"2024-03-31T14:59:55.863119Z","iopub.status.idle":"2024-03-31T14:59:55.890052Z","shell.execute_reply.started":"2024-03-31T14:59:55.863075Z","shell.execute_reply":"2024-03-31T14:59:55.888669Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0       -124.16     40.79                52.0       1264.0           277.0   \n1       -117.36     34.00                19.0       4592.0           895.0   \n2       -120.35     38.04                16.0       1499.0           326.0   \n3       -120.76     35.52                 7.0       9613.0          1666.0   \n4       -118.20     33.81                46.0       1388.0           254.0   \n...         ...       ...                 ...          ...             ...   \n3428    -118.32     33.98                44.0       1448.0           314.0   \n3429    -118.36     33.86                36.0        681.0           122.0   \n3430    -121.91     36.99                23.0       5675.0           964.0   \n3431    -122.49     37.79                52.0       3146.0           478.0   \n3432    -118.42     33.97                44.0       1462.0           240.0   \n\n      population  households  median_income  median_house_value  \\\n0          591.0       284.0         1.7778             76900.0   \n1         2769.0       838.0         3.3622            105100.0   \n2          733.0       286.0         2.5729            118800.0   \n3         4487.0      1653.0         3.6667            250600.0   \n4          742.0       241.0         4.6458            212100.0   \n...          ...         ...            ...                 ...   \n3428       861.0       310.0         2.2396            108600.0   \n3429       360.0       128.0         5.2799            332600.0   \n3430      2197.0       880.0         4.8693            322300.0   \n3431      1143.0       455.0         6.1407            500001.0   \n3432       562.0       237.0         4.9375            365200.0   \n\n     ocean_proximity  \n0         NEAR OCEAN  \n1             INLAND  \n2             INLAND  \n3          <1H OCEAN  \n4         NEAR OCEAN  \n...              ...  \n3428       <1H OCEAN  \n3429       <1H OCEAN  \n3430      NEAR OCEAN  \n3431        NEAR BAY  \n3432       <1H OCEAN  \n\n[3433 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-124.16</td>\n      <td>40.79</td>\n      <td>52.0</td>\n      <td>1264.0</td>\n      <td>277.0</td>\n      <td>591.0</td>\n      <td>284.0</td>\n      <td>1.7778</td>\n      <td>76900.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-117.36</td>\n      <td>34.00</td>\n      <td>19.0</td>\n      <td>4592.0</td>\n      <td>895.0</td>\n      <td>2769.0</td>\n      <td>838.0</td>\n      <td>3.3622</td>\n      <td>105100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-120.35</td>\n      <td>38.04</td>\n      <td>16.0</td>\n      <td>1499.0</td>\n      <td>326.0</td>\n      <td>733.0</td>\n      <td>286.0</td>\n      <td>2.5729</td>\n      <td>118800.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-120.76</td>\n      <td>35.52</td>\n      <td>7.0</td>\n      <td>9613.0</td>\n      <td>1666.0</td>\n      <td>4487.0</td>\n      <td>1653.0</td>\n      <td>3.6667</td>\n      <td>250600.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-118.20</td>\n      <td>33.81</td>\n      <td>46.0</td>\n      <td>1388.0</td>\n      <td>254.0</td>\n      <td>742.0</td>\n      <td>241.0</td>\n      <td>4.6458</td>\n      <td>212100.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3428</th>\n      <td>-118.32</td>\n      <td>33.98</td>\n      <td>44.0</td>\n      <td>1448.0</td>\n      <td>314.0</td>\n      <td>861.0</td>\n      <td>310.0</td>\n      <td>2.2396</td>\n      <td>108600.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>3429</th>\n      <td>-118.36</td>\n      <td>33.86</td>\n      <td>36.0</td>\n      <td>681.0</td>\n      <td>122.0</td>\n      <td>360.0</td>\n      <td>128.0</td>\n      <td>5.2799</td>\n      <td>332600.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>3430</th>\n      <td>-121.91</td>\n      <td>36.99</td>\n      <td>23.0</td>\n      <td>5675.0</td>\n      <td>964.0</td>\n      <td>2197.0</td>\n      <td>880.0</td>\n      <td>4.8693</td>\n      <td>322300.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>3431</th>\n      <td>-122.49</td>\n      <td>37.79</td>\n      <td>52.0</td>\n      <td>3146.0</td>\n      <td>478.0</td>\n      <td>1143.0</td>\n      <td>455.0</td>\n      <td>6.1407</td>\n      <td>500001.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n    <tr>\n      <th>3432</th>\n      <td>-118.42</td>\n      <td>33.97</td>\n      <td>44.0</td>\n      <td>1462.0</td>\n      <td>240.0</td>\n      <td>562.0</td>\n      <td>237.0</td>\n      <td>4.9375</td>\n      <td>365200.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n  </tbody>\n</table>\n<p>3433 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### The dependent variable(the predictor) is the \"median_house_value\" so we will set it as y","metadata":{}},{"cell_type":"code","source":"y_train = train['median_house_value'].to_numpy()\ny_test = test['median_house_value'].to_numpy()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.891390Z","iopub.execute_input":"2024-03-31T14:59:55.894129Z","iopub.status.idle":"2024-03-31T14:59:55.901581Z","shell.execute_reply.started":"2024-03-31T14:59:55.894086Z","shell.execute_reply":"2024-03-31T14:59:55.900229Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"y_train , y_test","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.903417Z","iopub.execute_input":"2024-03-31T14:59:55.903788Z","iopub.status.idle":"2024-03-31T14:59:55.916932Z","shell.execute_reply.started":"2024-03-31T14:59:55.903756Z","shell.execute_reply":"2024-03-31T14:59:55.915567Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"(array([113700.,  63100., 221500., ..., 177400., 346000., 500001.]),\n array([ 76900., 105100., 118800., ..., 322300., 500001., 365200.]))"},"metadata":{}}]},{"cell_type":"code","source":"y_train.shape, y_test.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.918936Z","iopub.execute_input":"2024-03-31T14:59:55.919413Z","iopub.status.idle":"2024-03-31T14:59:55.928296Z","shell.execute_reply.started":"2024-03-31T14:59:55.919366Z","shell.execute_reply":"2024-03-31T14:59:55.927013Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"((17000,), (3433,))"},"metadata":{}}]},{"cell_type":"markdown","source":"## BASELINE MODEL\nWhat this baseline model essentially does is that it takes the average of all the house values in the train data and compares with the house value in test data.\n\nThe avg value will be a certain number and it would be compared with each test data to see how close the predictions are to the actual value","metadata":{}},{"cell_type":"code","source":"avg_house_value = train['median_house_value'].mean()\nprint(avg_house_value)\n\nbaseline_model_predictions = [avg_house_value] * len(test) #this is the value which will be be compared with all the values in test data","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.929917Z","iopub.execute_input":"2024-03-31T14:59:55.930262Z","iopub.status.idle":"2024-03-31T14:59:55.939657Z","shell.execute_reply.started":"2024-03-31T14:59:55.930233Z","shell.execute_reply":"2024-03-31T14:59:55.938301Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"207315.12364705882\n","output_type":"stream"}]},{"cell_type":"code","source":"# checking the error\nfrom sklearn.metrics import mean_absolute_error\n\nmean_absolute_error(baseline_model_predictions , test['median_house_value'] )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:55.941556Z","iopub.execute_input":"2024-03-31T14:59:55.941969Z","iopub.status.idle":"2024-03-31T14:59:57.377833Z","shell.execute_reply.started":"2024-03-31T14:59:55.941933Z","shell.execute_reply":"2024-03-31T14:59:57.376992Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"91317.03094871576"},"metadata":{}}]},{"cell_type":"markdown","source":"**This states that on average the base line model is off my 91K USD from the actual value. So in all sense its not a right model.**","metadata":{}},{"cell_type":"markdown","source":"## 1. DIMENSIONALITY REDUCTION (PCA)","metadata":{}},{"cell_type":"code","source":"# Find the correlation between imp features such as households, rooms and bedrooms\n\ntrain[['households','total_rooms','total_bedrooms']].corr()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:57.379358Z","iopub.execute_input":"2024-03-31T14:59:57.380028Z","iopub.status.idle":"2024-03-31T14:59:57.397024Z","shell.execute_reply.started":"2024-03-31T14:59:57.379989Z","shell.execute_reply":"2024-03-31T14:59:57.395621Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"                households  total_rooms  total_bedrooms\nhouseholds        1.000000     0.919161        0.980729\ntotal_rooms       0.919161     1.000000        0.929533\ntotal_bedrooms    0.980729     0.929533        1.000000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>households</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>households</th>\n      <td>1.000000</td>\n      <td>0.919161</td>\n      <td>0.980729</td>\n    </tr>\n    <tr>\n      <th>total_rooms</th>\n      <td>0.919161</td>\n      <td>1.000000</td>\n      <td>0.929533</td>\n    </tr>\n    <tr>\n      <th>total_bedrooms</th>\n      <td>0.980729</td>\n      <td>0.929533</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Correlation values are always between -1 to 1. If the values are positive, like above, it means the variables have positive correlation among them. As the number of total_rooms increases, total_bedrooms increases as well. It will be linear upward graph.\n\nNow, why did we do this? As we can see there's so much positive correlation betwn these variables, these all are going to have almost the same effect on the median_house_value. Yes, you can just use one of the variables among these but a more suggested method to use is PCA.","metadata":{}},{"cell_type":"code","source":"# Before we begin, we'll use only these 3 variables in a df without PCA\n\nx_train_3 = train[['households','total_rooms','total_bedrooms']].to_numpy()\nx_train_3","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:57.398350Z","iopub.execute_input":"2024-03-31T14:59:57.398680Z","iopub.status.idle":"2024-03-31T14:59:57.409473Z","shell.execute_reply.started":"2024-03-31T14:59:57.398650Z","shell.execute_reply":"2024-03-31T14:59:57.408451Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"array([[ 459., 4495.,  856.],\n       [ 503., 2419.,  605.],\n       [ 746., 3912.,  737.],\n       ...,\n       [ 535., 2157.,  554.],\n       [ 334., 1921.,  394.],\n       [ 189., 1327.,  190.]])"},"metadata":{}}]},{"cell_type":"code","source":"x_train_3.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:57.410862Z","iopub.execute_input":"2024-03-31T14:59:57.411390Z","iopub.status.idle":"2024-03-31T14:59:57.419992Z","shell.execute_reply.started":"2024-03-31T14:59:57.411358Z","shell.execute_reply":"2024-03-31T14:59:57.418833Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"(17000, 3)"},"metadata":{}}]},{"cell_type":"code","source":"# similarly with the test data\nx_test_3 = test[['households','total_rooms','total_bedrooms']].to_numpy()\nx_test_3.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:57.421657Z","iopub.execute_input":"2024-03-31T14:59:57.422015Z","iopub.status.idle":"2024-03-31T14:59:57.430408Z","shell.execute_reply.started":"2024-03-31T14:59:57.421988Z","shell.execute_reply":"2024-03-31T14:59:57.429505Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"(3433, 3)"},"metadata":{}}]},{"cell_type":"code","source":"# getting error on random forest with training on (x_train_3, y_train) and testing on (x_test_3, y_test)\n\nfrom sklearn.ensemble import RandomForestRegressor\nrf = RandomForestRegressor(n_estimators = 40, max_depth = 5).fit(x_train_3, y_train)\nrf_predictions = rf.predict(x_test_3)\n\nmean_absolute_error(y_test, rf_predictions )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:57.431548Z","iopub.execute_input":"2024-03-31T14:59:57.432143Z","iopub.status.idle":"2024-03-31T14:59:58.533971Z","shell.execute_reply.started":"2024-03-31T14:59:57.432113Z","shell.execute_reply":"2024-03-31T14:59:58.532646Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"81986.03287564534"},"metadata":{}}]},{"cell_type":"markdown","source":"On avg the model was off by 81k USD from actual values","metadata":{}},{"cell_type":"code","source":"# now let's if PCA - linear dimensionality reduction technique with 2 feature output helps for this particular df \nfrom sklearn.decomposition import PCA\n\npca = PCA(n_components = 2) # 2 here means that output 2 columns with the neccessary values.\npca = pca.fit(x_train_3) # in our case we feed 3 columns, but through pca we get 2 columns with concise values of all 3 columns\n                         # through this we wont further need 3 columns, we'd need only 2\n\npca","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:58.535478Z","iopub.execute_input":"2024-03-31T14:59:58.535910Z","iopub.status.idle":"2024-03-31T14:59:58.579805Z","shell.execute_reply.started":"2024-03-31T14:59:58.535874Z","shell.execute_reply":"2024-03-31T14:59:58.578588Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"PCA(n_components=2)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"x_train_pca = pca.transform(x_train_3)\nx_test_pca = pca.transform(x_test_3)","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:58.581415Z","iopub.execute_input":"2024-03-31T14:59:58.582165Z","iopub.status.idle":"2024-03-31T14:59:58.590251Z","shell.execute_reply.started":"2024-03-31T14:59:58.582120Z","shell.execute_reply":"2024-03-31T14:59:58.589063Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"x_train_pca","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:58.592440Z","iopub.execute_input":"2024-03-31T14:59:58.593463Z","iopub.status.idle":"2024-03-31T14:59:58.604223Z","shell.execute_reply.started":"2024-03-31T14:59:58.593411Z","shell.execute_reply":"2024-03-31T14:59:58.602842Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array([[ 1860.26046989,  -243.94589907],\n       [ -194.18524519,   100.51463046],\n       [ 1318.16332961,     5.01126614],\n       ...,\n       [ -452.67894231,   148.43578611],\n       [ -741.81000914,   -43.55172458],\n       [-1377.70665169,  -143.84503669]])"},"metadata":{}}]},{"cell_type":"code","source":"x_test_pca","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:58.606421Z","iopub.execute_input":"2024-03-31T14:59:58.606912Z","iopub.status.idle":"2024-03-31T14:59:58.617922Z","shell.execute_reply.started":"2024-03-31T14:59:58.606873Z","shell.execute_reply":"2024-03-31T14:59:58.616736Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"array([[-1408.66956331,    -4.01506108],\n       [ 2021.19536026,    17.06981121],\n       [-1171.39641008,   -23.93302372],\n       ...,\n       [ 3092.34103365,  -161.79414325],\n       [  482.42807596,  -191.93947101],\n       [-1230.17159663,  -108.32616905]])"},"metadata":{}}]},{"cell_type":"code","source":"# testing the new pca data with earlier random forest regessor\nfrom sklearn.ensemble import RandomForestRegressor\nrf_pca = RandomForestRegressor(n_estimators = 40, max_depth = 5).fit(x_train_pca, y_train)\nrf_pca_predictions = rf_pca.predict(x_test_pca)\n\nmean_absolute_error(y_test, rf_pca_predictions )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:58.621745Z","iopub.execute_input":"2024-03-31T14:59:58.623305Z","iopub.status.idle":"2024-03-31T14:59:59.343961Z","shell.execute_reply.started":"2024-03-31T14:59:58.623247Z","shell.execute_reply":"2024-03-31T14:59:59.342782Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"80171.23543719461"},"metadata":{}}]},{"cell_type":"markdown","source":"So, the PCA did help. Maybe if we change it to linear regg or some other fine tuning in columns, can give better results as well.","metadata":{}},{"cell_type":"markdown","source":"## 2. PREPROCESSING / SCALING","metadata":{}},{"cell_type":"markdown","source":"### Usually when values are far apart from each other(some values are in 10000s whereas some are in 100s), this creates problems for model esp deep learning models. So, to avoid this we use scaling to make the values in somewhat closer range with eachother.","metadata":{}},{"cell_type":"code","source":"x_train_3","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:59.351016Z","iopub.execute_input":"2024-03-31T14:59:59.351430Z","iopub.status.idle":"2024-03-31T14:59:59.359307Z","shell.execute_reply.started":"2024-03-31T14:59:59.351398Z","shell.execute_reply":"2024-03-31T14:59:59.357887Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"array([[ 459., 4495.,  856.],\n       [ 503., 2419.,  605.],\n       [ 746., 3912.,  737.],\n       ...,\n       [ 535., 2157.,  554.],\n       [ 334., 1921.,  394.],\n       [ 189., 1327.,  190.]])"},"metadata":{}}]},{"cell_type":"markdown","source":" So here is you see, the ones with large values will be considered of higher importance by the model. but that shouldnt be the case because we know all 3 are pretty much of same importance and have positive correlation among eachother. so when we scale all the values are brought down to similar ranges, which tells the model that all the columns are more or less of same importance.","metadata":{}},{"cell_type":"code","source":"# different preprocessing approaches on x_train_3\n\n# 1. Standard Scaling\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer\n\nscaler = StandardScaler().fit(x_train_3)\nx_train_3_scaled = scaler.transform(x_train_3)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:59.361627Z","iopub.execute_input":"2024-03-31T14:59:59.361987Z","iopub.status.idle":"2024-03-31T14:59:59.370986Z","shell.execute_reply.started":"2024-03-31T14:59:59.361958Z","shell.execute_reply":"2024-03-31T14:59:59.370128Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"x_train_3_scaled ","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:59.372098Z","iopub.execute_input":"2024-03-31T14:59:59.372450Z","iopub.status.idle":"2024-03-31T14:59:59.381740Z","shell.execute_reply.started":"2024-03-31T14:59:59.372421Z","shell.execute_reply":"2024-03-31T14:59:59.380611Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"array([[-0.10354708,  0.85030177,  0.75626789],\n       [ 0.01108939, -0.09711996,  0.16137372],\n       [ 0.64419539,  0.58423873,  0.47422643],\n       ...,\n       [ 0.09446138, -0.2166886 ,  0.04049881],\n       [-0.42921889, -0.32439165, -0.3387166 ],\n       [-0.80699819, -0.59547474, -0.82221624]])"},"metadata":{}}]},{"cell_type":"markdown","source":"Underlying concept of scaler - it takes each value and subtracts with the mean of the whole column and divides by standard deviation. this is done for each value in the column.\nSo, in the above example the values now also have negative values and are majorly in between -3 to 3(checked through plotting) ","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.hist(x_train_3_scaled[:,0]) # just the first column","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:59.383630Z","iopub.execute_input":"2024-03-31T14:59:59.384724Z","iopub.status.idle":"2024-03-31T14:59:59.692052Z","shell.execute_reply.started":"2024-03-31T14:59:59.384675Z","shell.execute_reply":"2024-03-31T14:59:59.690662Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"(array([1.2838e+04, 3.4180e+03, 5.3200e+02, 1.2200e+02, 5.0000e+01,\n        1.7000e+01, 1.3000e+01, 5.0000e+00, 4.0000e+00, 1.0000e+00]),\n array([-1.29680859,  0.28751962,  1.87184784,  3.45617605,  5.04050426,\n         6.62483248,  8.20916069,  9.7934889 , 11.37781712, 12.96214533,\n        14.54647354]),\n <BarContainer object of 10 artists>)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# 2. Normalisation \n\nnormalise = Normalizer().fit(x_train_3)\nx_train_3_normalised = normalise.transform(x_train_3)\n\nplt.hist(x_train_3_normalised[:,0])","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:59.693602Z","iopub.execute_input":"2024-03-31T14:59:59.694040Z","iopub.status.idle":"2024-03-31T14:59:59.977707Z","shell.execute_reply.started":"2024-03-31T14:59:59.693998Z","shell.execute_reply":"2024-03-31T14:59:59.976403Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"(array([9.400e+01, 7.660e+02, 8.491e+03, 6.129e+03, 1.193e+03, 2.490e+02,\n        5.800e+01, 1.300e+01, 5.000e+00, 2.000e+00]),\n array([0.00693435, 0.06768906, 0.12844377, 0.18919848, 0.24995319,\n        0.31070789, 0.3714626 , 0.43221731, 0.49297202, 0.55372673,\n        0.61448144]),\n <BarContainer object of 10 artists>)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"# 3. MinMaxScaler\n\nminmax = MinMaxScaler().fit(x_train_3)\nx_train_3_minmax = minmax.transform(x_train_3)\n\nplt.hist(x_train_3_minmax[:,0])","metadata":{"execution":{"iopub.status.busy":"2024-03-31T14:59:59.979204Z","iopub.execute_input":"2024-03-31T14:59:59.979612Z","iopub.status.idle":"2024-03-31T15:00:00.246471Z","shell.execute_reply.started":"2024-03-31T14:59:59.979575Z","shell.execute_reply":"2024-03-31T15:00:00.245245Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"(array([1.2838e+04, 3.4180e+03, 5.3200e+02, 1.2200e+02, 5.0000e+01,\n        1.7000e+01, 1.3000e+01, 5.0000e+00, 4.0000e+00, 1.0000e+00]),\n array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n <BarContainer object of 10 artists>)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"So it completely depends on your problem statement which you'd prefer over other. Try all of them out and see what works the best. Mostly you will have to standardize your data. When you know that there's a max value out there, suppose image data, which would have values between 0 and 255, you'd want to use MinMax scaler\nYou'd think why? since the all values are in similar range, why to standarise them? this is because most of the ml algorithms, neural networks tend to perform quite good when the values are betwn 0 - 1, -3 or 3, just something small like that.","metadata":{}},{"cell_type":"code","source":"x_train_3_scaled ","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:00.247778Z","iopub.execute_input":"2024-03-31T15:00:00.248072Z","iopub.status.idle":"2024-03-31T15:00:00.255230Z","shell.execute_reply.started":"2024-03-31T15:00:00.248048Z","shell.execute_reply":"2024-03-31T15:00:00.254172Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"array([[-0.10354708,  0.85030177,  0.75626789],\n       [ 0.01108939, -0.09711996,  0.16137372],\n       [ 0.64419539,  0.58423873,  0.47422643],\n       ...,\n       [ 0.09446138, -0.2166886 ,  0.04049881],\n       [-0.42921889, -0.32439165, -0.3387166 ],\n       [-0.80699819, -0.59547474, -0.82221624]])"},"metadata":{}}]},{"cell_type":"code","source":"x_test_3_scaled = scaler.transform(x_test_3)\n","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:00.256599Z","iopub.execute_input":"2024-03-31T15:00:00.256938Z","iopub.status.idle":"2024-03-31T15:00:00.265040Z","shell.execute_reply.started":"2024-03-31T15:00:00.256909Z","shell.execute_reply":"2024-03-31T15:00:00.264067Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"x_test_3_scaled ","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:00.266379Z","iopub.execute_input":"2024-03-31T15:00:00.266965Z","iopub.status.idle":"2024-03-31T15:00:00.278682Z","shell.execute_reply.started":"2024-03-31T15:00:00.266929Z","shell.execute_reply":"2024-03-31T15:00:00.277380Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"array([[-0.55948762, -0.62422598, -0.61601786],\n       [ 0.88388984,  0.89456955,  0.84870165],\n       [-0.55427687, -0.5169793 , -0.49988315],\n       ...,\n       [ 0.99331557,  1.38881701,  1.0122383 ],\n       [-0.11396858,  0.23466019, -0.13962851],\n       [-0.68194021, -0.53386495, -0.70371143]])"},"metadata":{}}]},{"cell_type":"code","source":"# testing the new scaled data with earlier random forest regessor\n\nrf_scaled  = RandomForestRegressor(n_estimators = 40, max_depth = 5).fit(x_train_3_scaled , y_train)\nrf_scaled_predictions = rf_scaled.predict(x_test_3_scaled)\n\nmean_absolute_error(y_test, rf_scaled_predictions )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:00.280236Z","iopub.execute_input":"2024-03-31T15:00:00.280564Z","iopub.status.idle":"2024-03-31T15:00:01.009037Z","shell.execute_reply.started":"2024-03-31T15:00:00.280538Z","shell.execute_reply":"2024-03-31T15:00:01.007714Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"81707.48856036112"},"metadata":{}}]},{"cell_type":"markdown","source":"Predicted values are 81k off from the actual values. Honestly, this shows that RF isnt really interested with scaling But, remember Neural Networks prioritise it a lot.","metadata":{}},{"cell_type":"code","source":"### Turns out the PCA prefers having the data first be normalized and then fed for dimensionality reduction. So, we create a pipeline which would\n### Pipeline -- Normalization -> PCA -> RF","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.010481Z","iopub.execute_input":"2024-03-31T15:00:01.010829Z","iopub.status.idle":"2024-03-31T15:00:01.017451Z","shell.execute_reply.started":"2024-03-31T15:00:01.010799Z","shell.execute_reply":"2024-03-31T15:00:01.015778Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"from sklearn.pipeline import Pipeline\n\nnormalize_pca_rf = Pipeline(steps=[('Normalizer', Normalizer()),('PCA',PCA(n_components = 2)),('RF',RandomForestRegressor(n_estimators = 40, max_depth = 5))])\nnormalize_pca_rf","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.019577Z","iopub.execute_input":"2024-03-31T15:00:01.020294Z","iopub.status.idle":"2024-03-31T15:00:01.043271Z","shell.execute_reply.started":"2024-03-31T15:00:01.020248Z","shell.execute_reply":"2024-03-31T15:00:01.042016Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"Pipeline(steps=[('Normalizer', Normalizer()), ('PCA', PCA(n_components=2)),\n                ('RF', RandomForestRegressor(max_depth=5, n_estimators=40))])","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;Normalizer&#x27;, Normalizer()), (&#x27;PCA&#x27;, PCA(n_components=2)),\n                (&#x27;RF&#x27;, RandomForestRegressor(max_depth=5, n_estimators=40))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;Normalizer&#x27;, Normalizer()), (&#x27;PCA&#x27;, PCA(n_components=2)),\n                (&#x27;RF&#x27;, RandomForestRegressor(max_depth=5, n_estimators=40))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Normalizer</label><div class=\"sk-toggleable__content\"><pre>Normalizer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=2)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=5, n_estimators=40)</pre></div></div></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"normalize_pca_rf.fit(x_train_3 , y_train)\nnormalize_pca_rf_predictions = normalize_pca_rf.predict(x_test_3)","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.045447Z","iopub.execute_input":"2024-03-31T15:00:01.045818Z","iopub.status.idle":"2024-03-31T15:00:01.824022Z","shell.execute_reply.started":"2024-03-31T15:00:01.045785Z","shell.execute_reply":"2024-03-31T15:00:01.821385Z"},"trusted":true},"execution_count":40,"outputs":[]},{"cell_type":"code","source":"mean_absolute_error(y_test, normalize_pca_rf_predictions )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.826914Z","iopub.execute_input":"2024-03-31T15:00:01.827475Z","iopub.status.idle":"2024-03-31T15:00:01.839788Z","shell.execute_reply.started":"2024-03-31T15:00:01.827425Z","shell.execute_reply":"2024-03-31T15:00:01.837429Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"78230.4281775273"},"metadata":{}}]},{"cell_type":"markdown","source":"The values are a bit better this time (drop from 82k to 78k). Similarly you can try and test different approaches and see which performs the best.","metadata":{}},{"cell_type":"markdown","source":"# 3. CATEGORICAL ENCODING (ONE HOT/ DUMMY)","metadata":{}},{"cell_type":"markdown","source":"#### dummy - pandas version \n#### one hot - numpy version\n#### both perform the same thing\n#### dummy encoding is done when there are categorical values, and each such value has to be given a numerical value.\n#### but this isnt used in all cases for ex. Words. Words are encoded using Embeddings instead of dummy because what dummy does is it considers all the categories as seperate entities but suppose you are analysing a sentence, there are words that are similar to each other, you cant do dummy because that would just ignore the similarity among the words itself","metadata":{}},{"cell_type":"code","source":"train","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.841928Z","iopub.execute_input":"2024-03-31T15:00:01.842385Z","iopub.status.idle":"2024-03-31T15:00:01.872867Z","shell.execute_reply.started":"2024-03-31T15:00:01.842329Z","shell.execute_reply":"2024-03-31T15:00:01.871654Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0        -120.37     38.25                13.0       4495.0           856.0   \n1        -121.45     38.62                38.0       2419.0           605.0   \n2        -121.80     37.27                17.0       3912.0           737.0   \n3        -120.25     38.55                15.0       4403.0           891.0   \n4        -117.73     34.07                33.0       1921.0           489.0   \n...          ...       ...                 ...          ...             ...   \n16995    -118.43     34.16                34.0       2459.0           489.0   \n16996    -117.07     32.77                38.0       3779.0           614.0   \n16997    -117.13     32.81                19.0       2157.0           554.0   \n16998    -119.51     34.39                32.0       1921.0           394.0   \n16999    -122.14     37.43                52.0       1327.0           190.0   \n\n       population  households  median_income  median_house_value  \\\n0          1149.0       459.0         2.5352            113700.0   \n1          1696.0       503.0         1.4861             63100.0   \n2          2693.0       746.0         5.0772            221500.0   \n3          1103.0       433.0         3.0125            111700.0   \n4          1430.0       467.0         2.3406            122600.0   \n...           ...         ...            ...                 ...   \n16995      1139.0       463.0         4.0347            353600.0   \n16996      1495.0       614.0         4.3529            184000.0   \n16997      1349.0       535.0         2.7652            177400.0   \n16998       951.0       334.0         3.2330            346000.0   \n16999       467.0       189.0        12.5902            500001.0   \n\n      ocean_proximity  \n0              INLAND  \n1              INLAND  \n2           <1H OCEAN  \n3              INLAND  \n4              INLAND  \n...               ...  \n16995       <1H OCEAN  \n16996      NEAR OCEAN  \n16997      NEAR OCEAN  \n16998      NEAR OCEAN  \n16999        NEAR BAY  \n\n[17000 rows x 10 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-120.37</td>\n      <td>38.25</td>\n      <td>13.0</td>\n      <td>4495.0</td>\n      <td>856.0</td>\n      <td>1149.0</td>\n      <td>459.0</td>\n      <td>2.5352</td>\n      <td>113700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-121.45</td>\n      <td>38.62</td>\n      <td>38.0</td>\n      <td>2419.0</td>\n      <td>605.0</td>\n      <td>1696.0</td>\n      <td>503.0</td>\n      <td>1.4861</td>\n      <td>63100.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-121.80</td>\n      <td>37.27</td>\n      <td>17.0</td>\n      <td>3912.0</td>\n      <td>737.0</td>\n      <td>2693.0</td>\n      <td>746.0</td>\n      <td>5.0772</td>\n      <td>221500.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-120.25</td>\n      <td>38.55</td>\n      <td>15.0</td>\n      <td>4403.0</td>\n      <td>891.0</td>\n      <td>1103.0</td>\n      <td>433.0</td>\n      <td>3.0125</td>\n      <td>111700.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-117.73</td>\n      <td>34.07</td>\n      <td>33.0</td>\n      <td>1921.0</td>\n      <td>489.0</td>\n      <td>1430.0</td>\n      <td>467.0</td>\n      <td>2.3406</td>\n      <td>122600.0</td>\n      <td>INLAND</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>16995</th>\n      <td>-118.43</td>\n      <td>34.16</td>\n      <td>34.0</td>\n      <td>2459.0</td>\n      <td>489.0</td>\n      <td>1139.0</td>\n      <td>463.0</td>\n      <td>4.0347</td>\n      <td>353600.0</td>\n      <td>&lt;1H OCEAN</td>\n    </tr>\n    <tr>\n      <th>16996</th>\n      <td>-117.07</td>\n      <td>32.77</td>\n      <td>38.0</td>\n      <td>3779.0</td>\n      <td>614.0</td>\n      <td>1495.0</td>\n      <td>614.0</td>\n      <td>4.3529</td>\n      <td>184000.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>16997</th>\n      <td>-117.13</td>\n      <td>32.81</td>\n      <td>19.0</td>\n      <td>2157.0</td>\n      <td>554.0</td>\n      <td>1349.0</td>\n      <td>535.0</td>\n      <td>2.7652</td>\n      <td>177400.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>16998</th>\n      <td>-119.51</td>\n      <td>34.39</td>\n      <td>32.0</td>\n      <td>1921.0</td>\n      <td>394.0</td>\n      <td>951.0</td>\n      <td>334.0</td>\n      <td>3.2330</td>\n      <td>346000.0</td>\n      <td>NEAR OCEAN</td>\n    </tr>\n    <tr>\n      <th>16999</th>\n      <td>-122.14</td>\n      <td>37.43</td>\n      <td>52.0</td>\n      <td>1327.0</td>\n      <td>190.0</td>\n      <td>467.0</td>\n      <td>189.0</td>\n      <td>12.5902</td>\n      <td>500001.0</td>\n      <td>NEAR BAY</td>\n    </tr>\n  </tbody>\n</table>\n<p>17000 rows × 10 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# in this dataset though, it makes sense to use dummy encoding for 'ocean_proximity' \n# because all the categories are different from each other\n\ntrain_dummies = pd.get_dummies(train['ocean_proximity']).astype(int)\ntrain_dummies.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.874617Z","iopub.execute_input":"2024-03-31T15:00:01.875009Z","iopub.status.idle":"2024-03-31T15:00:01.894760Z","shell.execute_reply.started":"2024-03-31T15:00:01.874975Z","shell.execute_reply":"2024-03-31T15:00:01.893768Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"   <1H OCEAN  INLAND  ISLAND  NEAR BAY  NEAR OCEAN\n0          0       1       0         0           0\n1          0       1       0         0           0\n2          1       0       0         0           0\n3          0       1       0         0           0\n4          0       1       0         0           0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# concatenate the dummy variables to the training data\n\ntrain = pd.concat([train, train_dummies] , axis = 1)\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.896060Z","iopub.execute_input":"2024-03-31T15:00:01.897447Z","iopub.status.idle":"2024-03-31T15:00:01.924474Z","shell.execute_reply.started":"2024-03-31T15:00:01.897403Z","shell.execute_reply":"2024-03-31T15:00:01.923249Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -120.37     38.25                13.0       4495.0           856.0   \n1    -121.45     38.62                38.0       2419.0           605.0   \n2    -121.80     37.27                17.0       3912.0           737.0   \n3    -120.25     38.55                15.0       4403.0           891.0   \n4    -117.73     34.07                33.0       1921.0           489.0   \n\n   population  households  median_income  median_house_value ocean_proximity  \\\n0      1149.0       459.0         2.5352            113700.0          INLAND   \n1      1696.0       503.0         1.4861             63100.0          INLAND   \n2      2693.0       746.0         5.0772            221500.0       <1H OCEAN   \n3      1103.0       433.0         3.0125            111700.0          INLAND   \n4      1430.0       467.0         2.3406            122600.0          INLAND   \n\n   <1H OCEAN  INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n0          0       1       0         0           0  \n1          0       1       0         0           0  \n2          1       0       0         0           0  \n3          0       1       0         0           0  \n4          0       1       0         0           0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>ocean_proximity</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-120.37</td>\n      <td>38.25</td>\n      <td>13.0</td>\n      <td>4495.0</td>\n      <td>856.0</td>\n      <td>1149.0</td>\n      <td>459.0</td>\n      <td>2.5352</td>\n      <td>113700.0</td>\n      <td>INLAND</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-121.45</td>\n      <td>38.62</td>\n      <td>38.0</td>\n      <td>2419.0</td>\n      <td>605.0</td>\n      <td>1696.0</td>\n      <td>503.0</td>\n      <td>1.4861</td>\n      <td>63100.0</td>\n      <td>INLAND</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-121.80</td>\n      <td>37.27</td>\n      <td>17.0</td>\n      <td>3912.0</td>\n      <td>737.0</td>\n      <td>2693.0</td>\n      <td>746.0</td>\n      <td>5.0772</td>\n      <td>221500.0</td>\n      <td>&lt;1H OCEAN</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-120.25</td>\n      <td>38.55</td>\n      <td>15.0</td>\n      <td>4403.0</td>\n      <td>891.0</td>\n      <td>1103.0</td>\n      <td>433.0</td>\n      <td>3.0125</td>\n      <td>111700.0</td>\n      <td>INLAND</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-117.73</td>\n      <td>34.07</td>\n      <td>33.0</td>\n      <td>1921.0</td>\n      <td>489.0</td>\n      <td>1430.0</td>\n      <td>467.0</td>\n      <td>2.3406</td>\n      <td>122600.0</td>\n      <td>INLAND</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"train['ocean_proximity'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.926708Z","iopub.execute_input":"2024-03-31T15:00:01.927166Z","iopub.status.idle":"2024-03-31T15:00:01.943880Z","shell.execute_reply.started":"2024-03-31T15:00:01.927129Z","shell.execute_reply":"2024-03-31T15:00:01.941906Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"ocean_proximity\n<1H OCEAN     7561\nINLAND        5349\nNEAR OCEAN    2191\nNEAR BAY      1896\nISLAND           3\nName: count, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"notice island just has 3 values, but since it is a category, there would be a extra column just for it. ","metadata":{}},{"cell_type":"code","source":"train.drop(['ocean_proximity','ISLAND'], inplace = True , axis = 1)","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.945672Z","iopub.execute_input":"2024-03-31T15:00:01.946319Z","iopub.status.idle":"2024-03-31T15:00:01.962664Z","shell.execute_reply.started":"2024-03-31T15:00:01.946270Z","shell.execute_reply":"2024-03-31T15:00:01.960861Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.964379Z","iopub.execute_input":"2024-03-31T15:00:01.965049Z","iopub.status.idle":"2024-03-31T15:00:01.993695Z","shell.execute_reply.started":"2024-03-31T15:00:01.965002Z","shell.execute_reply":"2024-03-31T15:00:01.992723Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -120.37     38.25                13.0       4495.0           856.0   \n1    -121.45     38.62                38.0       2419.0           605.0   \n2    -121.80     37.27                17.0       3912.0           737.0   \n3    -120.25     38.55                15.0       4403.0           891.0   \n4    -117.73     34.07                33.0       1921.0           489.0   \n\n   population  households  median_income  median_house_value  <1H OCEAN  \\\n0      1149.0       459.0         2.5352            113700.0          0   \n1      1696.0       503.0         1.4861             63100.0          0   \n2      2693.0       746.0         5.0772            221500.0          1   \n3      1103.0       433.0         3.0125            111700.0          0   \n4      1430.0       467.0         2.3406            122600.0          0   \n\n   INLAND  NEAR BAY  NEAR OCEAN  \n0       1         0           0  \n1       1         0           0  \n2       0         0           0  \n3       1         0           0  \n4       1         0           0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-120.37</td>\n      <td>38.25</td>\n      <td>13.0</td>\n      <td>4495.0</td>\n      <td>856.0</td>\n      <td>1149.0</td>\n      <td>459.0</td>\n      <td>2.5352</td>\n      <td>113700.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-121.45</td>\n      <td>38.62</td>\n      <td>38.0</td>\n      <td>2419.0</td>\n      <td>605.0</td>\n      <td>1696.0</td>\n      <td>503.0</td>\n      <td>1.4861</td>\n      <td>63100.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-121.80</td>\n      <td>37.27</td>\n      <td>17.0</td>\n      <td>3912.0</td>\n      <td>737.0</td>\n      <td>2693.0</td>\n      <td>746.0</td>\n      <td>5.0772</td>\n      <td>221500.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-120.25</td>\n      <td>38.55</td>\n      <td>15.0</td>\n      <td>4403.0</td>\n      <td>891.0</td>\n      <td>1103.0</td>\n      <td>433.0</td>\n      <td>3.0125</td>\n      <td>111700.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-117.73</td>\n      <td>34.07</td>\n      <td>33.0</td>\n      <td>1921.0</td>\n      <td>489.0</td>\n      <td>1430.0</td>\n      <td>467.0</td>\n      <td>2.3406</td>\n      <td>122600.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# similarly for the test data\n\ntest_dummies = pd.get_dummies(test['ocean_proximity']).astype(int)\ntest_dummies.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:01.995592Z","iopub.execute_input":"2024-03-31T15:00:01.996382Z","iopub.status.idle":"2024-03-31T15:00:02.013992Z","shell.execute_reply.started":"2024-03-31T15:00:01.996293Z","shell.execute_reply":"2024-03-31T15:00:02.012917Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"   <1H OCEAN  INLAND  ISLAND  NEAR BAY  NEAR OCEAN\n0          0       0       0         0           1\n1          0       1       0         0           0\n2          0       1       0         0           0\n3          1       0       0         0           0\n4          0       0       0         0           1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test = pd.concat([test, test_dummies] , axis = 1)\ntest.drop(['ocean_proximity'], inplace = True , axis = 1)\ntest.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.015729Z","iopub.execute_input":"2024-03-31T15:00:02.016449Z","iopub.status.idle":"2024-03-31T15:00:02.044203Z","shell.execute_reply.started":"2024-03-31T15:00:02.016403Z","shell.execute_reply":"2024-03-31T15:00:02.042970Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -124.16     40.79                52.0       1264.0           277.0   \n1    -117.36     34.00                19.0       4592.0           895.0   \n2    -120.35     38.04                16.0       1499.0           326.0   \n3    -120.76     35.52                 7.0       9613.0          1666.0   \n4    -118.20     33.81                46.0       1388.0           254.0   \n\n   population  households  median_income  median_house_value  <1H OCEAN  \\\n0       591.0       284.0         1.7778             76900.0          0   \n1      2769.0       838.0         3.3622            105100.0          0   \n2       733.0       286.0         2.5729            118800.0          0   \n3      4487.0      1653.0         3.6667            250600.0          1   \n4       742.0       241.0         4.6458            212100.0          0   \n\n   INLAND  ISLAND  NEAR BAY  NEAR OCEAN  \n0       0       0         0           1  \n1       1       0         0           0  \n2       1       0         0           0  \n3       0       0         0           0  \n4       0       0         0           1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>ISLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-124.16</td>\n      <td>40.79</td>\n      <td>52.0</td>\n      <td>1264.0</td>\n      <td>277.0</td>\n      <td>591.0</td>\n      <td>284.0</td>\n      <td>1.7778</td>\n      <td>76900.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-117.36</td>\n      <td>34.00</td>\n      <td>19.0</td>\n      <td>4592.0</td>\n      <td>895.0</td>\n      <td>2769.0</td>\n      <td>838.0</td>\n      <td>3.3622</td>\n      <td>105100.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-120.35</td>\n      <td>38.04</td>\n      <td>16.0</td>\n      <td>1499.0</td>\n      <td>326.0</td>\n      <td>733.0</td>\n      <td>286.0</td>\n      <td>2.5729</td>\n      <td>118800.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-120.76</td>\n      <td>35.52</td>\n      <td>7.0</td>\n      <td>9613.0</td>\n      <td>1666.0</td>\n      <td>4487.0</td>\n      <td>1653.0</td>\n      <td>3.6667</td>\n      <td>250600.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-118.20</td>\n      <td>33.81</td>\n      <td>46.0</td>\n      <td>1388.0</td>\n      <td>254.0</td>\n      <td>742.0</td>\n      <td>241.0</td>\n      <td>4.6458</td>\n      <td>212100.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"test.drop(['ISLAND'], inplace = True , axis = 1)","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.046233Z","iopub.execute_input":"2024-03-31T15:00:02.046981Z","iopub.status.idle":"2024-03-31T15:00:02.053662Z","shell.execute_reply.started":"2024-03-31T15:00:02.046941Z","shell.execute_reply":"2024-03-31T15:00:02.052043Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"markdown","source":"### Just for curiosity I will be training a model just on these dummy variables. Ofcourse later on we will utilize the whole dataset.","metadata":{}},{"cell_type":"code","source":"x_train_dummies = train.to_numpy()[:,-4:] # just the last 4 columns\nx_train_dummies","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.055324Z","iopub.execute_input":"2024-03-31T15:00:02.055840Z","iopub.status.idle":"2024-03-31T15:00:02.071981Z","shell.execute_reply.started":"2024-03-31T15:00:02.055785Z","shell.execute_reply":"2024-03-31T15:00:02.069659Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"array([[0., 1., 0., 0.],\n       [0., 1., 0., 0.],\n       [1., 0., 0., 0.],\n       ...,\n       [0., 0., 0., 1.],\n       [0., 0., 0., 1.],\n       [0., 0., 1., 0.]])"},"metadata":{}}]},{"cell_type":"code","source":"x_test_dummies = test.to_numpy()[:,-4:] # just the last 4 columns\nx_test_dummies","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.073951Z","iopub.execute_input":"2024-03-31T15:00:02.075221Z","iopub.status.idle":"2024-03-31T15:00:02.084243Z","shell.execute_reply.started":"2024-03-31T15:00:02.075177Z","shell.execute_reply":"2024-03-31T15:00:02.083173Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"array([[0., 0., 0., 1.],\n       [0., 1., 0., 0.],\n       [0., 1., 0., 0.],\n       ...,\n       [0., 0., 0., 1.],\n       [0., 0., 1., 0.],\n       [1., 0., 0., 0.]])"},"metadata":{}}]},{"cell_type":"code","source":"# using linear regression this time\n\nfrom sklearn.linear_model import LinearRegression\nlr_dummy = LinearRegression().fit(x_train_dummies, y_train)\nlr_dummy_predictions = lr_dummy.predict(x_test_dummies)\n\nmean_absolute_error(y_test, lr_dummy_predictions )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.085774Z","iopub.execute_input":"2024-03-31T15:00:02.086175Z","iopub.status.idle":"2024-03-31T15:00:02.122073Z","shell.execute_reply.started":"2024-03-31T15:00:02.086143Z","shell.execute_reply":"2024-03-31T15:00:02.119838Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"75691.38107776093"},"metadata":{}}]},{"cell_type":"markdown","source":"just by using the dummy variables, the model is able to perform good.","metadata":{}},{"cell_type":"markdown","source":"# 4. BINNING (GROUPING, AGGREGATING)","metadata":{}},{"cell_type":"code","source":"# So sometimes instead of scaling or pca, what we can do for some columns esp age data for instance is is to create bins or groups\n# as we can see below, the \"housing_median_age\" can be binned for some values","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.124464Z","iopub.execute_input":"2024-03-31T15:00:02.125823Z","iopub.status.idle":"2024-03-31T15:00:02.131604Z","shell.execute_reply.started":"2024-03-31T15:00:02.125751Z","shell.execute_reply":"2024-03-31T15:00:02.129827Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"train['housing_median_age'].hist()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.133475Z","iopub.execute_input":"2024-03-31T15:00:02.133976Z","iopub.status.idle":"2024-03-31T15:00:02.496920Z","shell.execute_reply.started":"2024-03-31T15:00:02.133934Z","shell.execute_reply":"2024-03-31T15:00:02.495678Z"},"trusted":true},"execution_count":55,"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":"<Axes: >"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"train['house_age_less_than_30'] = (train['housing_median_age'] < 30).astype(int) # 30 is just an example. you can bin differently as well\n\n# there are other ways as well like mutliple variable say age less than 20 then 0, age betwn  20 to 40 -> 1, age above 40 -> 2. this can be done through one hot encoding","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.499717Z","iopub.execute_input":"2024-03-31T15:00:02.500217Z","iopub.status.idle":"2024-03-31T15:00:02.509625Z","shell.execute_reply.started":"2024-03-31T15:00:02.500174Z","shell.execute_reply":"2024-03-31T15:00:02.507703Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.511371Z","iopub.execute_input":"2024-03-31T15:00:02.512558Z","iopub.status.idle":"2024-03-31T15:00:02.542493Z","shell.execute_reply.started":"2024-03-31T15:00:02.512515Z","shell.execute_reply":"2024-03-31T15:00:02.540640Z"},"trusted":true},"execution_count":57,"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n0    -120.37     38.25                13.0       4495.0           856.0   \n1    -121.45     38.62                38.0       2419.0           605.0   \n2    -121.80     37.27                17.0       3912.0           737.0   \n3    -120.25     38.55                15.0       4403.0           891.0   \n4    -117.73     34.07                33.0       1921.0           489.0   \n\n   population  households  median_income  median_house_value  <1H OCEAN  \\\n0      1149.0       459.0         2.5352            113700.0          0   \n1      1696.0       503.0         1.4861             63100.0          0   \n2      2693.0       746.0         5.0772            221500.0          1   \n3      1103.0       433.0         3.0125            111700.0          0   \n4      1430.0       467.0         2.3406            122600.0          0   \n\n   INLAND  NEAR BAY  NEAR OCEAN  house_age_less_than_30  \n0       1         0           0                       1  \n1       1         0           0                       0  \n2       0         0           0                       1  \n3       1         0           0                       1  \n4       1         0           0                       0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>longitude</th>\n      <th>latitude</th>\n      <th>housing_median_age</th>\n      <th>total_rooms</th>\n      <th>total_bedrooms</th>\n      <th>population</th>\n      <th>households</th>\n      <th>median_income</th>\n      <th>median_house_value</th>\n      <th>&lt;1H OCEAN</th>\n      <th>INLAND</th>\n      <th>NEAR BAY</th>\n      <th>NEAR OCEAN</th>\n      <th>house_age_less_than_30</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-120.37</td>\n      <td>38.25</td>\n      <td>13.0</td>\n      <td>4495.0</td>\n      <td>856.0</td>\n      <td>1149.0</td>\n      <td>459.0</td>\n      <td>2.5352</td>\n      <td>113700.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-121.45</td>\n      <td>38.62</td>\n      <td>38.0</td>\n      <td>2419.0</td>\n      <td>605.0</td>\n      <td>1696.0</td>\n      <td>503.0</td>\n      <td>1.4861</td>\n      <td>63100.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-121.80</td>\n      <td>37.27</td>\n      <td>17.0</td>\n      <td>3912.0</td>\n      <td>737.0</td>\n      <td>2693.0</td>\n      <td>746.0</td>\n      <td>5.0772</td>\n      <td>221500.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-120.25</td>\n      <td>38.55</td>\n      <td>15.0</td>\n      <td>4403.0</td>\n      <td>891.0</td>\n      <td>1103.0</td>\n      <td>433.0</td>\n      <td>3.0125</td>\n      <td>111700.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-117.73</td>\n      <td>34.07</td>\n      <td>33.0</td>\n      <td>1921.0</td>\n      <td>489.0</td>\n      <td>1430.0</td>\n      <td>467.0</td>\n      <td>2.3406</td>\n      <td>122600.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"# new training dataset with just the age values\n\nx_train_median_age30 = train['house_age_less_than_30'].to_numpy().reshape(-1,1)\nx_train_median_age30.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.544431Z","iopub.execute_input":"2024-03-31T15:00:02.545447Z","iopub.status.idle":"2024-03-31T15:00:02.559403Z","shell.execute_reply.started":"2024-03-31T15:00:02.545406Z","shell.execute_reply":"2024-03-31T15:00:02.558124Z"},"trusted":true},"execution_count":58,"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"(17000, 1)"},"metadata":{}}]},{"cell_type":"code","source":"# new test dataset with just the age values\n\ntest['house_age_less_than_30'] = (test['housing_median_age'] < 30).astype(int)\nx_test_median_age30 = test['house_age_less_than_30'].to_numpy().reshape(-1,1)\nx_test_median_age30.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.560951Z","iopub.execute_input":"2024-03-31T15:00:02.561325Z","iopub.status.idle":"2024-03-31T15:00:02.576302Z","shell.execute_reply.started":"2024-03-31T15:00:02.561291Z","shell.execute_reply":"2024-03-31T15:00:02.574977Z"},"trusted":true},"execution_count":59,"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"(3433, 1)"},"metadata":{}}]},{"cell_type":"code","source":"# training a linear model on age value\n\nlr_median_age30 = LinearRegression().fit(x_train_median_age30, y_train)\nlr_median_age30_predictions = lr_median_age30.predict(x_test_median_age30)\n\nmean_absolute_error(y_test, lr_median_age30_predictions )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.578489Z","iopub.execute_input":"2024-03-31T15:00:02.578874Z","iopub.status.idle":"2024-03-31T15:00:02.591777Z","shell.execute_reply.started":"2024-03-31T15:00:02.578842Z","shell.execute_reply":"2024-03-31T15:00:02.590161Z"},"trusted":true},"execution_count":60,"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"91260.95099187501"},"metadata":{}}]},{"cell_type":"markdown","source":"So we can say that the age bining wasnt really of any great help or maybe we can try other combinations of bining the ages.","metadata":{}},{"cell_type":"markdown","source":"# 5. CLUSTERING","metadata":{}},{"cell_type":"code","source":"# Plot the scatter plot for lan and lat given in data set - which is of california\n\nplt.scatter(train['latitude'], train['longitude'])","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.593717Z","iopub.execute_input":"2024-03-31T15:00:02.594070Z","iopub.status.idle":"2024-03-31T15:00:02.946531Z","shell.execute_reply.started":"2024-03-31T15:00:02.594042Z","shell.execute_reply":"2024-03-31T15:00:02.945219Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"<matplotlib.collections.PathCollection at 0x7b4984a69300>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"x_train_lat_long = train[['latitude','longitude']].to_numpy()","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.948173Z","iopub.execute_input":"2024-03-31T15:00:02.948646Z","iopub.status.idle":"2024-03-31T15:00:02.955076Z","shell.execute_reply.started":"2024-03-31T15:00:02.948593Z","shell.execute_reply":"2024-03-31T15:00:02.953859Z"},"trusted":true},"execution_count":62,"outputs":[]},{"cell_type":"code","source":"# K Means model from the longitide and latitude and get clusters\n\nfrom sklearn.cluster import KMeans\n\nkmeans = KMeans(n_clusters = 5).fit(x_train_lat_long)\nkmeans.labels_  #array showing which value belongs to which cluster","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:02.956760Z","iopub.execute_input":"2024-03-31T15:00:02.957809Z","iopub.status.idle":"2024-03-31T15:00:04.322970Z","shell.execute_reply.started":"2024-03-31T15:00:02.957760Z","shell.execute_reply":"2024-03-31T15:00:04.321796Z"},"trusted":true},"execution_count":63,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n  warnings.warn(\n","output_type":"stream"},{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"array([1, 1, 1, ..., 0, 4, 1], dtype=int32)"},"metadata":{}}]},{"cell_type":"code","source":"#plotting the clusters\n\nimport plotly.express as px\n\npx.scatter(x=train['latitude'], y=train['longitude'], color = kmeans.labels_)","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:04.324596Z","iopub.execute_input":"2024-03-31T15:00:04.325606Z","iopub.status.idle":"2024-03-31T15:00:07.077583Z","shell.execute_reply.started":"2024-03-31T15:00:04.325563Z","shell.execute_reply":"2024-03-31T15:00:07.076410Z"},"trusted":true},"execution_count":64,"outputs":[{"output_type":"display_data","data":{"text/html":"        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) 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</div>"},"metadata":{}}]},{"cell_type":"code","source":"# now will apply one hot encoding for the said clusters\n\nx_train_dummy_clusters = pd.get_dummies(pd.Series(kmeans.labels_)).to_numpy().astype(int)\nx_train_dummy_clusters.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.079501Z","iopub.execute_input":"2024-03-31T15:00:07.080366Z","iopub.status.idle":"2024-03-31T15:00:07.090374Z","shell.execute_reply.started":"2024-03-31T15:00:07.080298Z","shell.execute_reply":"2024-03-31T15:00:07.089447Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"(17000, 5)"},"metadata":{}}]},{"cell_type":"code","source":"# similarly for test data, predict the clusters for x test and then one hot encoding\n\nx_test_lat_long = test[['latitude','longitude']].to_numpy()\nx_test_dummy_clusters = pd.get_dummies(pd.Series(kmeans.predict(x_test_lat_long))).to_numpy().astype(int)\nx_test_dummy_clusters.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.091532Z","iopub.execute_input":"2024-03-31T15:00:07.092029Z","iopub.status.idle":"2024-03-31T15:00:07.124326Z","shell.execute_reply.started":"2024-03-31T15:00:07.091999Z","shell.execute_reply":"2024-03-31T15:00:07.123199Z"},"trusted":true},"execution_count":66,"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":"(3433, 5)"},"metadata":{}}]},{"cell_type":"code","source":"# check error on the linear model only on the cluster one hot encodings\n\nlr_clustering = LinearRegression().fit(x_train_dummy_clusters , y_train)\nlr_clustering_predictions = lr_clustering.predict(x_test_dummy_clusters)\n\nmean_absolute_error(lr_clustering_predictions, y_test)","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.125533Z","iopub.execute_input":"2024-03-31T15:00:07.126086Z","iopub.status.idle":"2024-03-31T15:00:07.153546Z","shell.execute_reply.started":"2024-03-31T15:00:07.126057Z","shell.execute_reply":"2024-03-31T15:00:07.152247Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"82320.22953684824"},"metadata":{}}]},{"cell_type":"markdown","source":"# 6. COMBINATION OF FEATURE (FEATURE SELECTIONS)","metadata":{}},{"cell_type":"code","source":"# lets first take the shapes of all the training data we did feature engg on\n\nx_train_3_scaled.shape, x_train_pca.shape, x_train_dummy_clusters.shape , x_train_dummies.shape ","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.155732Z","iopub.execute_input":"2024-03-31T15:00:07.159749Z","iopub.status.idle":"2024-03-31T15:00:07.176239Z","shell.execute_reply.started":"2024-03-31T15:00:07.159684Z","shell.execute_reply":"2024-03-31T15:00:07.174512Z"},"trusted":true},"execution_count":68,"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"((17000, 3), (17000, 2), (17000, 5), (17000, 4))"},"metadata":{}}]},{"cell_type":"code","source":"# lets concatenate all the individual training datas into one big x_train_all\n\nx_train_all = np.concatenate([x_train_3_scaled, x_train_dummy_clusters , x_train_dummies], axis = 1)\nx_train_all.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.178986Z","iopub.execute_input":"2024-03-31T15:00:07.180014Z","iopub.status.idle":"2024-03-31T15:00:07.196678Z","shell.execute_reply.started":"2024-03-31T15:00:07.179963Z","shell.execute_reply":"2024-03-31T15:00:07.195256Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"(17000, 12)"},"metadata":{}}]},{"cell_type":"code","source":"# lets first take the shapes of all the test data we did feature engg on\n\nx_test_3_scaled.shape, x_test_pca.shape, x_test_dummy_clusters.shape , x_test_dummies.shape ","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.198886Z","iopub.execute_input":"2024-03-31T15:00:07.200134Z","iopub.status.idle":"2024-03-31T15:00:07.219919Z","shell.execute_reply.started":"2024-03-31T15:00:07.200073Z","shell.execute_reply":"2024-03-31T15:00:07.218586Z"},"trusted":true},"execution_count":70,"outputs":[{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"((3433, 3), (3433, 2), (3433, 5), (3433, 4))"},"metadata":{}}]},{"cell_type":"code","source":"# lets concatenate all the individual test datas into one big x_train_all\n\nx_test_all = np.concatenate([x_test_3_scaled, x_test_dummy_clusters , x_test_dummies], axis = 1)\nx_test_all.shape","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.222114Z","iopub.execute_input":"2024-03-31T15:00:07.223041Z","iopub.status.idle":"2024-03-31T15:00:07.235950Z","shell.execute_reply.started":"2024-03-31T15:00:07.222987Z","shell.execute_reply":"2024-03-31T15:00:07.234155Z"},"trusted":true},"execution_count":71,"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"(3433, 12)"},"metadata":{}}]},{"cell_type":"code","source":"# testing the new data with earlier random forest regessor\n\nrf_all = RandomForestRegressor(n_estimators = 40, max_depth = 5).fit(x_train_all, y_train)\nrf_all_predictions = rf_all.predict(x_test_all)\n\nmean_absolute_error(y_test, rf_all_predictions )","metadata":{"execution":{"iopub.status.busy":"2024-03-31T15:00:07.238122Z","iopub.execute_input":"2024-03-31T15:00:07.239046Z","iopub.status.idle":"2024-03-31T15:00:08.099272Z","shell.execute_reply.started":"2024-03-31T15:00:07.238996Z","shell.execute_reply":"2024-03-31T15:00:08.097993Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"67364.64158273162"},"metadata":{}}]},{"cell_type":"markdown","source":"There's a significant drop here. So, this feature engineering did some benefit for the model as the avg error went from 90k to 67k.","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}