exercise_2-your-first-machine-learning-model.ipynb

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**This notebook is an exercise in the [Introduction to Machine Learning](https://www.kaggle.com/learn/intro-to-machine-learning) course.  You can reference the tutorial at [this link](https://www.kaggle.com/dansbecker/your-first-machine-learning-model).**\n\n---\n","metadata":{}},{"cell_type":"markdown","source":"## Recap\nSo far, you have loaded your data and reviewed it with the following code. Run this cell to set up your coding environment where the previous step left off.","metadata":{}},{"cell_type":"code","source":"# Code you have previously used to load data\nimport pandas as pd\n\n# Path of the file to read\niowa_file_path = '../input/home-data-for-ml-course/train.csv'\n\nhome_data = pd.read_csv(iowa_file_path)\n\n# Set up code checking\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.machine_learning.ex3 import *\n\nprint(\"Setup Complete\")","metadata":{"execution":{"iopub.status.busy":"2021-12-12T07:55:08.204671Z","iopub.execute_input":"2021-12-12T07:55:08.205092Z","iopub.status.idle":"2021-12-12T07:55:09.663591Z","shell.execute_reply.started":"2021-12-12T07:55:08.204990Z","shell.execute_reply":"2021-12-12T07:55:09.661422Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"# Exercises\n\n## Step 1: Specify Prediction Target\nSelect the target variable, which corresponds to the sales price. Save this to a new variable called `y`. You'll need to print a list of the columns to find the name of the column you need.\n","metadata":{}},{"cell_type":"code","source":"# print the list of columns in the dataset to find the name of the prediction target\nhome_data.head()","metadata":{"execution":{"iopub.status.busy":"2021-12-12T07:56:56.842934Z","iopub.execute_input":"2021-12-12T07:56:56.843340Z","iopub.status.idle":"2021-12-12T07:56:56.883915Z","shell.execute_reply.started":"2021-12-12T07:56:56.843289Z","shell.execute_reply":"2021-12-12T07:56:56.882818Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"y = home_data.SalePrice\n\n# Check your answer\nstep_1.check()","metadata":{"execution":{"iopub.status.busy":"2021-12-12T07:57:38.405689Z","iopub.execute_input":"2021-12-12T07:57:38.406604Z","iopub.status.idle":"2021-12-12T07:57:38.419319Z","shell.execute_reply.started":"2021-12-12T07:57:38.406553Z","shell.execute_reply":"2021-12-12T07:57:38.418682Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"# The lines below will show you a hint or the solution.\n# step_1.hint() \n# step_1.solution()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 2: Create X\nNow you will create a DataFrame called `X` holding the predictive features.\n\nSince you want only some columns from the original data, you'll first create a list with the names of the columns you want in `X`.\n\nYou'll use just the following columns in the list (you can copy and paste the whole list to save some typing, though you'll still need to add quotes):\n    * LotArea\n    * YearBuilt\n    * 1stFlrSF\n    * 2ndFlrSF\n    * FullBath\n    * BedroomAbvGr\n    * TotRmsAbvGrd\n\nAfter you've created that list of features, use it to create the DataFrame that you'll use to fit the model.","metadata":{}},{"cell_type":"code","source":"# Create the list of features below\nfeature_names = [\"LotArea\", \"YearBuilt\", \"1stFlrSF\", \"2ndFlrSF\",\"FullBath\", \"BedroomAbvGr\", \"TotRmsAbvGrd\"]\n\n# Select data corresponding to features in feature_names\nX = home_data[feature_names]\n\n# Check your answer\nstep_2.check()","metadata":{"execution":{"iopub.status.busy":"2021-12-12T08:22:40.139161Z","iopub.execute_input":"2021-12-12T08:22:40.140739Z","iopub.status.idle":"2021-12-12T08:22:40.155717Z","shell.execute_reply.started":"2021-12-12T08:22:40.140672Z","shell.execute_reply":"2021-12-12T08:22:40.155069Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# step_2.hint()\n# step_2.solution()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Review Data\nBefore building a model, take a quick look at **X** to verify it looks sensible","metadata":{}},{"cell_type":"code","source":"# Review data\n# print description or statistics from X\nprint(X.describe())\n\n# print the top few lines\n#print(_)","metadata":{"execution":{"iopub.status.busy":"2021-12-12T08:22:58.598371Z","iopub.execute_input":"2021-12-12T08:22:58.598640Z","iopub.status.idle":"2021-12-12T08:22:58.634928Z","shell.execute_reply.started":"2021-12-12T08:22:58.598611Z","shell.execute_reply":"2021-12-12T08:22:58.633944Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"## Step 3: Specify and Fit Model\nCreate a `DecisionTreeRegressor` and save it iowa_model. Ensure you've done the relevant import from sklearn to run this command.\n\nThen fit the model you just created using the data in `X` and `y` that you saved above.","metadata":{}},{"cell_type":"code","source":"# from _ import _\n#specify the model. \nfrom sklearn.tree import DecisionTreeRegressor\n\n#For model reproducibility, set a numeric value for random_state when specifying the model\niowa_model = DecisionTreeRegressor(random_state=1)\n\n# Fit the model\niowa_model.fit(X, y)\n\n# Check your answer\nstep_3.check()","metadata":{"execution":{"iopub.status.busy":"2021-12-12T08:30:51.911370Z","iopub.execute_input":"2021-12-12T08:30:51.911672Z","iopub.status.idle":"2021-12-12T08:30:51.934661Z","shell.execute_reply.started":"2021-12-12T08:30:51.911642Z","shell.execute_reply":"2021-12-12T08:30:51.933943Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"# step_3.hint()\n# step_3.solution()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Step 4: Make Predictions\nMake predictions with the model's `predict` command using `X` as the data. Save the results to a variable called `predictions`.","metadata":{}},{"cell_type":"code","source":"predictions = iowa_model.predict(X)\nprint(predictions)\n\n# Check your answer\nstep_4.check()","metadata":{"execution":{"iopub.status.busy":"2021-12-12T08:31:02.883601Z","iopub.execute_input":"2021-12-12T08:31:02.884043Z","iopub.status.idle":"2021-12-12T08:31:02.899472Z","shell.execute_reply.started":"2021-12-12T08:31:02.883993Z","shell.execute_reply":"2021-12-12T08:31:02.898569Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"# step_4.hint()\n# step_4.solution()","metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Think About Your Results\n\nUse the `head` method to compare the top few predictions to the actual home values (in `y`) for those same homes. Anything surprising?\n","metadata":{}},{"cell_type":"code","source":"# You can write code in this cell\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"It's natural to ask how accurate the model's predictions will be and how you can improve that. That will be you're next step.\n\n# Keep Going\n\nYou are ready for **[Model Validation](https://www.kaggle.com/dansbecker/model-validation).**\n","metadata":{}},{"cell_type":"markdown","source":"---\n\n\n\n\n*Have questions or comments? Visit the [course discussion forum](https://www.kaggle.com/learn/intro-to-machine-learning/discussion) to chat with other learners.*","metadata":{}}]}