phone-repair-estimation.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/nikolamurgovski/phone-repair-estimation?scriptVersionId=185789147\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"d0854184","metadata":{"papermill":{"duration":0.011794,"end_time":"2024-06-27T17:29:03.006232","exception":false,"start_time":"2024-06-27T17:29:02.994438","status":"completed"},"tags":[]},"source":["# **Predicting Mobile Phone Repair Durations Using Machine Learning**\n","*Nikola Murgovski, <br>\n"," 89221036@student.upr.si <br>\n"," UP FAMNIT*\n","## **1.  Introduction to the Project**\n","In today's fast paced world, mobile phones have become an indispensable part of our daily lives. As reliance on these devices grows, so does the need for efficient and timely repairs when they inevitably break down. Estimating the duration of mobile phone repairs accurately is crucial for both service providers and customers, ensuring smooth operations and high satisfaction levels. This project, aims to analyze and predict the repair times for various mobile phone brands and models on data that I took from a company in North Macedonia, meaning this project can help the mobile service company in their work. We have a dataset that includes details about phone brands, models, the nature of problems, and repair timelines, we employ machine learning techniques to develop predictive models. These models help in estimating the time required to complete repairs based on historical data.\n","#### **1.1. Key Objectives of this project are:**\n","* **Develop predictive models**: Using machine learning algorithms, we create models to predict the repair duration for different phone brands.\n","* **Evaluate model performance**: We compare the effectiveness of various algorithms, including classification, decision trees and logistic regression, to determine the most accurate predictor.\n","* **Provide actionable insights**: The results of this analysis can assist the specific repair service that we got this dataset from in improving their operations, managing customer expectations, and optimizing resource allocation.\n","\n"]},{"cell_type":"code","execution_count":1,"id":"25e3d886","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:03.030352Z","iopub.status.busy":"2024-06-27T17:29:03.029982Z","iopub.status.idle":"2024-06-27T17:29:04.374165Z","shell.execute_reply":"2024-06-27T17:29:04.373019Z"},"papermill":{"duration":1.359029,"end_time":"2024-06-27T17:29:04.376677","exception":false,"start_time":"2024-06-27T17:29:03.017648","status":"completed"},"tags":[]},"outputs":[],"source":["# Importing libraries for data manipulation and numerical operations\n","import pandas as pd\n","import numpy as np"]},{"cell_type":"markdown","id":"bfd98aec","metadata":{"papermill":{"duration":0.010982,"end_time":"2024-06-27T17:29:04.398939","exception":false,"start_time":"2024-06-27T17:29:04.387957","status":"completed"},"tags":[]},"source":["## **2. The Dataset used**\n","The dataset that we used in this project is from a company located in North Macedonia, they are a mobile repair shop and they accepted my offer to analyze their data. I find this project more interesting since we are using some data from not such a big company. \n","#### **2.1. Dataset properties**\n","The dataset that we have comprises detailed records of mobile phone repairs, including essential attributes such as the phone brand, phone model, the date the phone was received for repair, the date it was ready, and the specific problem encountered. This dataset spans various brands and models, providing a comprehensive overview of repair activities. Each entry in the dataset represents an individual repair instance, allowing for the analysis of repair durations and the identification of patterns and trends. The data includes categorical variables representing the phone brand, model, and type of problem, as well as date variables from which repair duration is calculated.\n","#### **2.2. Exploring the data**\n","As we can see below, the columns are Phone Brand, Phone Model, Date Received, Date Ready and Phone Problem. The good thing about this dataset is that the dates for arrival and completion are well structured and it will be easier to work with it when we have to calculate the duration time. Also, the phone problems are categorical, they are not manually written which will make the process of analyzing and comparing easier for the model. Every row represents a individual repair and it clearly gives us the phone brand and phone model which is crucial for this project. <br>\n","At first sight the data we have looks good and well structured, but the data must be analyzed and cleaned so it doesn't affect the performance of the predicting models."]},{"cell_type":"code","execution_count":2,"id":"2bac7e7b","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.422254Z","iopub.status.busy":"2024-06-27T17:29:04.421756Z","iopub.status.idle":"2024-06-27T17:29:04.463563Z","shell.execute_reply":"2024-06-27T17:29:04.462428Z"},"papermill":{"duration":0.056193,"end_time":"2024-06-27T17:29:04.466018","exception":false,"start_time":"2024-06-27T17:29:04.409825","status":"completed"},"tags":[]},"outputs":[{"data":{"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>Phone Brand</th>\n","      <th>Phone Model</th>\n","      <th>Date Received</th>\n","      <th>Date Ready</th>\n","      <th>Phone Problem</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Samsung</td>\n","      <td>Galaxy S10</td>\n","      <td>2022-05-15</td>\n","      <td>2022-05-20</td>\n","      <td>LCD Replacement</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>iPhone</td>\n","      <td>12 Pro Max</td>\n","      <td>2022-06-10</td>\n","      <td>2022-06-15</td>\n","      <td>Battery Replacement</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Google</td>\n","      <td>Pixel 5</td>\n","      <td>2022-07-01</td>\n","      <td>2022-07-05</td>\n","      <td>Charging Port Repair</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Huawei</td>\n","      <td>P40</td>\n","      <td>2022-08-02</td>\n","      <td>2022-08-07</td>\n","      <td>Battery Replacement</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>OnePlus</td>\n","      <td>9T</td>\n","      <td>2022-09-05</td>\n","      <td>2022-09-10</td>\n","      <td>Camera Repair</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  Phone Brand Phone Model Date Received  Date Ready         Phone Problem\n","0     Samsung  Galaxy S10    2022-05-15  2022-05-20       LCD Replacement\n","1      iPhone  12 Pro Max    2022-06-10  2022-06-15   Battery Replacement\n","2      Google     Pixel 5    2022-07-01  2022-07-05  Charging Port Repair\n","3      Huawei         P40    2022-08-02  2022-08-07   Battery Replacement\n","4     OnePlus          9T    2022-09-05  2022-09-10         Camera Repair"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["# loading the dataset and displaying the first rows\n","df = pd.read_csv('/kaggle/input/dataset-sfs/dataset-sfs.csv')\n","df.head()"]},{"cell_type":"markdown","id":"0a0ce112","metadata":{"papermill":{"duration":0.011181,"end_time":"2024-06-27T17:29:04.488612","exception":false,"start_time":"2024-06-27T17:29:04.477431","status":"completed"},"tags":[]},"source":["## **3. Data Preprocessing**\n","Data Preprocessing is crucial for any ML project, it's for cleaning data, formatting and organizing raw data before it is used for the model. We do this step to be sure that the data we have doesnt have missing values or inconsistencies that can negatively impact the performance of the model.\n","#### **3.1 Missing Values**\n","Firstly we are searching for missing values, as we can see below the dataset doesnt have zero values in any column. By executing the function below, I confirmed that the dataset was free from any missing values, which is critical for the accuracy and validity of subsequent analyses and machine learning modeling."]},{"cell_type":"code","execution_count":3,"id":"e204c710","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.512629Z","iopub.status.busy":"2024-06-27T17:29:04.512207Z","iopub.status.idle":"2024-06-27T17:29:04.521683Z","shell.execute_reply":"2024-06-27T17:29:04.520327Z"},"papermill":{"duration":0.024411,"end_time":"2024-06-27T17:29:04.524191","exception":false,"start_time":"2024-06-27T17:29:04.49978","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Phone Brand      0\n","Phone Model      0\n","Date Received    0\n","Date Ready       0\n","Phone Problem    0\n","dtype: int64\n"]}],"source":["# check for missing values, zero values in each column\n","print(df.isnull().sum())"]},{"cell_type":"markdown","id":"1630547d","metadata":{"papermill":{"duration":0.011197,"end_time":"2024-06-27T17:29:04.549091","exception":false,"start_time":"2024-06-27T17:29:04.537894","status":"completed"},"tags":[]},"source":["#### **3.2 One-Hot Encoding**\n","We are using one-hot encoding technique to convert categorical variables into a format that can improve the model performance later. Many machine learning algorithms cannot directly work with categorical data, and thus, one-hot encoding is applied to transform these categorical variables into numerical format.\n","We are applying one-hot encoding to columns: Phone Brand, Phone Model and Phone Problem. The function call below creates new columns for each category in the categorical columns we chose. Each categorical column is replaced with boolean values true or false (binary values). With doing this we created 335 columns and data is hard to read for a human eye but easier for the algorithm and model to compute."]},{"cell_type":"code","execution_count":4,"id":"94aaa8da","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.573014Z","iopub.status.busy":"2024-06-27T17:29:04.572628Z","iopub.status.idle":"2024-06-27T17:29:04.635159Z","shell.execute_reply":"2024-06-27T17:29:04.634097Z"},"papermill":{"duration":0.077568,"end_time":"2024-06-27T17:29:04.637453","exception":false,"start_time":"2024-06-27T17:29:04.559885","status":"completed"},"tags":[]},"outputs":[{"data":{"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>Date Received</th>\n","      <th>Date Ready</th>\n","      <th>Phone Brand_Google</th>\n","      <th>Phone Brand_Huawei</th>\n","      <th>Phone Brand_OnePlus</th>\n","      <th>Phone Brand_Oppo</th>\n","      <th>Phone Brand_Samsung</th>\n","      <th>Phone Brand_Xiaomi</th>\n","      <th>Phone Brand_iPhone</th>\n","      <th>Phone Model_10</th>\n","      <th>...</th>\n","      <th>Phone Model_Y9 Prime 2019</th>\n","      <th>Phone Model_Y9s</th>\n","      <th>Phone Problem_Battery Replacement</th>\n","      <th>Phone Problem_Camera Repair</th>\n","      <th>Phone Problem_Charging Port Repair</th>\n","      <th>Phone Problem_LCD Replacement</th>\n","      <th>Phone Problem_Screen Repair</th>\n","      <th>Phone Problem_Screen Replacement</th>\n","      <th>Phone Problem_Speaker Replacement</th>\n","      <th>Phone Problem_Water Damage Repair</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2022-05-15</td>\n","      <td>2022-05-20</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2022-06-10</td>\n","      <td>2022-06-15</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2022-07-01</td>\n","      <td>2022-07-05</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2022-08-02</td>\n","      <td>2022-08-07</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2022-09-05</td>\n","      <td>2022-09-10</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 335 columns</p>\n","</div>"],"text/plain":["  Date Received  Date Ready  Phone Brand_Google  Phone Brand_Huawei  \\\n","0    2022-05-15  2022-05-20               False               False   \n","1    2022-06-10  2022-06-15               False               False   \n","2    2022-07-01  2022-07-05                True               False   \n","3    2022-08-02  2022-08-07               False                True   \n","4    2022-09-05  2022-09-10               False               False   \n","\n","   Phone Brand_OnePlus  Phone Brand_Oppo  Phone Brand_Samsung  \\\n","0                False             False                 True   \n","1                False             False                False   \n","2                False             False                False   \n","3                False             False                False   \n","4                 True             False                False   \n","\n","   Phone Brand_Xiaomi  Phone Brand_iPhone  Phone Model_10  ...  \\\n","0               False               False           False  ...   \n","1               False                True           False  ...   \n","2               False               False           False  ...   \n","3               False               False           False  ...   \n","4               False               False           False  ...   \n","\n","   Phone Model_Y9 Prime 2019  Phone Model_Y9s  \\\n","0                      False            False   \n","1                      False            False   \n","2                      False            False   \n","3                      False            False   \n","4                      False            False   \n","\n","   Phone Problem_Battery Replacement  Phone Problem_Camera Repair  \\\n","0                              False                        False   \n","1                               True                        False   \n","2                              False                        False   \n","3                               True                        False   \n","4                              False                         True   \n","\n","   Phone Problem_Charging Port Repair  Phone Problem_LCD Replacement  \\\n","0                               False                           True   \n","1                               False                          False   \n","2                                True                          False   \n","3                               False                          False   \n","4                               False                          False   \n","\n","   Phone Problem_Screen Repair  Phone Problem_Screen Replacement  \\\n","0                        False                             False   \n","1                        False                             False   \n","2                        False                             False   \n","3                        False                             False   \n","4                        False                             False   \n","\n","   Phone Problem_Speaker Replacement  Phone Problem_Water Damage Repair  \n","0                              False                              False  \n","1                              False                              False  \n","2                              False                              False  \n","3                              False                              False  \n","4                              False                              False  \n","\n","[5 rows x 335 columns]"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# one-hot encoding on categorical variables \n","df = pd.get_dummies(df, columns=['Phone Brand','Phone Model', 'Phone Problem'])\n","df.head()"]},{"cell_type":"markdown","id":"5db0995f","metadata":{"papermill":{"duration":0.011378,"end_time":"2024-06-27T17:29:04.660879","exception":false,"start_time":"2024-06-27T17:29:04.649501","status":"completed"},"tags":[]},"source":["#### **3.3 Convert date columns to get duration**\n","In our analysis, converting the 'Date Received' and 'Date Ready' column to datetime format is crucial for standardizing the date entries across the dataset. This transformation ensures that all dates are uniformly represented and can be accurately processed for further analysis. \n","\n","#### **3.3.1 Transforming the 'Date Recieved' column**\n","Subsequently, we extract additional temporal features from the 'Date Received' column to enrich our dataset. By deriving 'Month Received', we introduce new dimensions to our data. These feature enable us to explore potential patterns in repair durations across different months of the year. This preprocessing step not only enhances the granularity of our analysis but also prepares the data for modelling, which can effectively leverage temporal insights to predict repair durations accurately. The resulting dataset provides a comprehensive foundation for investigating factors influencing repair times and optimizing service operations in the mobile phone repair industry."]},{"cell_type":"code","execution_count":5,"id":"c5d726a3","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.685839Z","iopub.status.busy":"2024-06-27T17:29:04.68543Z","iopub.status.idle":"2024-06-27T17:29:04.725629Z","shell.execute_reply":"2024-06-27T17:29:04.724625Z"},"papermill":{"duration":0.055723,"end_time":"2024-06-27T17:29:04.728075","exception":false,"start_time":"2024-06-27T17:29:04.672352","status":"completed"},"tags":[]},"outputs":[{"data":{"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>Date Received</th>\n","      <th>Date Ready</th>\n","      <th>Phone Brand_Google</th>\n","      <th>Phone Brand_Huawei</th>\n","      <th>Phone Brand_OnePlus</th>\n","      <th>Phone Brand_Oppo</th>\n","      <th>Phone Brand_Samsung</th>\n","      <th>Phone Brand_Xiaomi</th>\n","      <th>Phone Brand_iPhone</th>\n","      <th>Phone Model_10</th>\n","      <th>...</th>\n","      <th>Phone Model_Y9s</th>\n","      <th>Phone Problem_Battery Replacement</th>\n","      <th>Phone Problem_Camera Repair</th>\n","      <th>Phone Problem_Charging Port Repair</th>\n","      <th>Phone Problem_LCD Replacement</th>\n","      <th>Phone Problem_Screen Repair</th>\n","      <th>Phone Problem_Screen Replacement</th>\n","      <th>Phone Problem_Speaker Replacement</th>\n","      <th>Phone Problem_Water Damage Repair</th>\n","      <th>Month Received</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2022-05-15</td>\n","      <td>2022-05-20</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2022-06-10</td>\n","      <td>2022-06-15</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2022-07-01</td>\n","      <td>2022-07-05</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>7</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2022-08-02</td>\n","      <td>2022-08-07</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2022-09-05</td>\n","      <td>2022-09-10</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>9</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 336 columns</p>\n","</div>"],"text/plain":["  Date Received  Date Ready  Phone Brand_Google  Phone Brand_Huawei  \\\n","0    2022-05-15  2022-05-20               False               False   \n","1    2022-06-10  2022-06-15               False               False   \n","2    2022-07-01  2022-07-05                True               False   \n","3    2022-08-02  2022-08-07               False                True   \n","4    2022-09-05  2022-09-10               False               False   \n","\n","   Phone Brand_OnePlus  Phone Brand_Oppo  Phone Brand_Samsung  \\\n","0                False             False                 True   \n","1                False             False                False   \n","2                False             False                False   \n","3                False             False                False   \n","4                 True             False                False   \n","\n","   Phone Brand_Xiaomi  Phone Brand_iPhone  Phone Model_10  ...  \\\n","0               False               False           False  ...   \n","1               False                True           False  ...   \n","2               False               False           False  ...   \n","3               False               False           False  ...   \n","4               False               False           False  ...   \n","\n","   Phone Model_Y9s  Phone Problem_Battery Replacement  \\\n","0            False                              False   \n","1            False                               True   \n","2            False                              False   \n","3            False                               True   \n","4            False                              False   \n","\n","   Phone Problem_Camera Repair  Phone Problem_Charging Port Repair  \\\n","0                        False                               False   \n","1                        False                               False   \n","2                        False                                True   \n","3                        False                               False   \n","4                         True                               False   \n","\n","   Phone Problem_LCD Replacement  Phone Problem_Screen Repair  \\\n","0                           True                        False   \n","1                          False                        False   \n","2                          False                        False   \n","3                          False                        False   \n","4                          False                        False   \n","\n","   Phone Problem_Screen Replacement  Phone Problem_Speaker Replacement  \\\n","0                             False                              False   \n","1                             False                              False   \n","2                             False                              False   \n","3                             False                              False   \n","4                             False                              False   \n","\n","   Phone Problem_Water Damage Repair  Month Received  \n","0                              False               5  \n","1                              False               6  \n","2                              False               7  \n","3                              False               8  \n","4                              False               9  \n","\n","[5 rows x 336 columns]"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Convert 'Date Received' to datetime format\n","df['Date Received'] = pd.to_datetime(df['Date Received'])\n","\n","# Extract additional features from 'Date Received'\n","df['Month Received'] = df['Date Received'].dt.month\n","df.head()"]},{"cell_type":"markdown","id":"3f076d20","metadata":{"papermill":{"duration":0.011847,"end_time":"2024-06-27T17:29:04.75236","exception":false,"start_time":"2024-06-27T17:29:04.740513","status":"completed"},"tags":[]},"source":["#### **3.3.2 Transforming the 'Date Ready' column to get Repair Duration**\n","We are doing the same thing for the 'Date Ready' column by transforming it into datetime format using the pandas library. And based on the columns for 'Date Recieved' and 'Date Ready' converted into datetime format we can calculate the 'Repair Duration' for every repair. For the 'Date Ready' column we are not extracting the month or the day of the week since it is not necessary for later. We will only be using the Month for receiving date of repairs, with that later we can see if repair durations vary based on the time of the year. Knowing the month when repairs are initiated allows the service provider to set realistic expectations for customers regarding repair times. It also helps by identifying potential delays based on historical data.\n","##### **Removing negative values**\n","We have to filter the dataframe to include only rows where duration time is not lower than 0, meaning we are removing values that are maybe not correctly inputed into the data or some miss calculations are done in the preprocessing, doing this step we will have correct durations."]},{"cell_type":"code","execution_count":6,"id":"0beefab7","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.778747Z","iopub.status.busy":"2024-06-27T17:29:04.778354Z","iopub.status.idle":"2024-06-27T17:29:04.818798Z","shell.execute_reply":"2024-06-27T17:29:04.817748Z"},"papermill":{"duration":0.056823,"end_time":"2024-06-27T17:29:04.821589","exception":false,"start_time":"2024-06-27T17:29:04.764766","status":"completed"},"tags":[]},"outputs":[{"data":{"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>Date Received</th>\n","      <th>Date Ready</th>\n","      <th>Phone Brand_Google</th>\n","      <th>Phone Brand_Huawei</th>\n","      <th>Phone Brand_OnePlus</th>\n","      <th>Phone Brand_Oppo</th>\n","      <th>Phone Brand_Samsung</th>\n","      <th>Phone Brand_Xiaomi</th>\n","      <th>Phone Brand_iPhone</th>\n","      <th>Phone Model_10</th>\n","      <th>...</th>\n","      <th>Phone Problem_Battery Replacement</th>\n","      <th>Phone Problem_Camera Repair</th>\n","      <th>Phone Problem_Charging Port Repair</th>\n","      <th>Phone Problem_LCD Replacement</th>\n","      <th>Phone Problem_Screen Repair</th>\n","      <th>Phone Problem_Screen Replacement</th>\n","      <th>Phone Problem_Speaker Replacement</th>\n","      <th>Phone Problem_Water Damage Repair</th>\n","      <th>Month Received</th>\n","      <th>Repair Duration</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2022-05-15</td>\n","      <td>2022-05-20</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>5</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2022-06-10</td>\n","      <td>2022-06-15</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>6</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2022-07-01</td>\n","      <td>2022-07-05</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>7</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2022-08-02</td>\n","      <td>2022-08-07</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>8</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2022-09-05</td>\n","      <td>2022-09-10</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>9</td>\n","      <td>5</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","      <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","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>97</th>\n","      <td>2022-12-29</td>\n","      <td>2022-12-31</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>12</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>98</th>\n","      <td>2022-12-31</td>\n","      <td>2023-01-02</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>12</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>99</th>\n","      <td>2023-01-02</td>\n","      <td>2023-01-04</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>100</th>\n","      <td>2023-01-04</td>\n","      <td>2023-01-06</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>101</th>\n","      <td>2023-01-06</td>\n","      <td>2023-01-08</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>True</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>100 rows × 337 columns</p>\n","</div>"],"text/plain":["    Date Received Date Ready  Phone Brand_Google  Phone Brand_Huawei  \\\n","0      2022-05-15 2022-05-20               False               False   \n","1      2022-06-10 2022-06-15               False               False   \n","2      2022-07-01 2022-07-05                True               False   \n","3      2022-08-02 2022-08-07               False                True   \n","4      2022-09-05 2022-09-10               False               False   \n","..            ...        ...                 ...                 ...   \n","97     2022-12-29 2022-12-31               False               False   \n","98     2022-12-31 2023-01-02               False               False   \n","99     2023-01-02 2023-01-04               False               False   \n","100    2023-01-04 2023-01-06               False               False   \n","101    2023-01-06 2023-01-08                True               False   \n","\n","     Phone Brand_OnePlus  Phone Brand_Oppo  Phone Brand_Samsung  \\\n","0                  False             False                 True   \n","1                  False             False                False   \n","2                  False             False                False   \n","3                  False             False                False   \n","4                   True             False                False   \n","..                   ...               ...                  ...   \n","97                 False             False                False   \n","98                 False             False                False   \n","99                 False             False                 True   \n","100                False             False                False   \n","101                False             False                False   \n","\n","     Phone Brand_Xiaomi  Phone Brand_iPhone  Phone Model_10  ...  \\\n","0                 False               False           False  ...   \n","1                 False                True           False  ...   \n","2                 False               False           False  ...   \n","3                 False               False           False  ...   \n","4                 False               False           False  ...   \n","..                  ...                 ...             ...  ...   \n","97                False                True           False  ...   \n","98                 True               False           False  ...   \n","99                False               False           False  ...   \n","100               False                True           False  ...   \n","101               False               False           False  ...   \n","\n","     Phone Problem_Battery Replacement  Phone Problem_Camera Repair  \\\n","0                                False                        False   \n","1                                 True                        False   \n","2                                False                        False   \n","3                                 True                        False   \n","4                                False                         True   \n","..                                 ...                          ...   \n","97                                True                        False   \n","98                               False                        False   \n","99                                True                        False   \n","100                              False                        False   \n","101                              False                         True   \n","\n","     Phone Problem_Charging Port Repair  Phone Problem_LCD Replacement  \\\n","0                                 False                           True   \n","1                                 False                          False   \n","2                                  True                          False   \n","3                                 False                          False   \n","4                                 False                          False   \n","..                                  ...                            ...   \n","97                                False                          False   \n","98                                 True                          False   \n","99                                False                          False   \n","100                               False                          False   \n","101                               False                          False   \n","\n","     Phone Problem_Screen Repair  Phone Problem_Screen Replacement  \\\n","0                          False                             False   \n","1                          False                             False   \n","2                          False                             False   \n","3                          False                             False   \n","4                          False                             False   \n","..                           ...                               ...   \n","97                         False                             False   \n","98                         False                             False   \n","99                         False                             False   \n","100                        False                              True   \n","101                        False                             False   \n","\n","     Phone Problem_Speaker Replacement  Phone Problem_Water Damage Repair  \\\n","0                                False                              False   \n","1                                False                              False   \n","2                                False                              False   \n","3                                False                              False   \n","4                                False                              False   \n","..                                 ...                                ...   \n","97                               False                              False   \n","98                               False                              False   \n","99                               False                              False   \n","100                              False                              False   \n","101                              False                              False   \n","\n","     Month Received  Repair Duration  \n","0                 5                5  \n","1                 6                5  \n","2                 7                4  \n","3                 8                5  \n","4                 9                5  \n","..              ...              ...  \n","97               12                2  \n","98               12                2  \n","99                1                2  \n","100               1                2  \n","101               1                2  \n","\n","[100 rows x 337 columns]"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Convert 'Date Ready' to datetime format\n","df['Date Ready'] = pd.to_datetime(df['Date Ready'])\n","\n","# Calculate the repair duration in days\n","df['Repair Duration'] = (df['Date Ready'] - df['Date Received']).dt.days\n","\n","# Removing negative values of repair duration\n","df = df[df['Repair Duration'] >= 0]\n","df.head(100)"]},{"cell_type":"code","execution_count":7,"id":"aad3b7a1","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.849602Z","iopub.status.busy":"2024-06-27T17:29:04.84922Z","iopub.status.idle":"2024-06-27T17:29:04.859256Z","shell.execute_reply":"2024-06-27T17:29:04.858314Z"},"papermill":{"duration":0.026278,"end_time":"2024-06-27T17:29:04.861565","exception":false,"start_time":"2024-06-27T17:29:04.835287","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Empty DataFrame\n","Columns: [Date Received, Date Ready, Phone Brand_Google, Phone Brand_Huawei, Phone Brand_OnePlus, Phone Brand_Oppo, Phone Brand_Samsung, Phone Brand_Xiaomi, Phone Brand_iPhone, Phone Model_10, Phone Model_11, Phone Model_11 Pro, Phone Model_12, Phone Model_12 Mini, Phone Model_12 Pro, Phone Model_12 Pro Max, Phone Model_13, Phone Model_13 Mini, Phone Model_13 Pro, Phone Model_14, Phone Model_15, Phone Model_16, Phone Model_17, Phone Model_18, Phone Model_19, Phone Model_2, Phone Model_20, Phone Model_21, Phone Model_22, Phone Model_23, Phone Model_24, Phone Model_3T, Phone Model_5T, Phone Model_6, Phone Model_6T, Phone Model_6s, Phone Model_7, Phone Model_7 Pro, Phone Model_7T, Phone Model_7T Pro, Phone Model_8, Phone Model_8 Pro, Phone Model_8T, Phone Model_9, Phone Model_9 Pro, Phone Model_9T, Phone Model_Black Shark 2, Phone Model_Black Shark 3, Phone Model_F1, Phone Model_Find X10, Phone Model_Find X11, Phone Model_Find X2, Phone Model_Find X2 Lite, Phone Model_Find X2 Pro, Phone Model_Find X3, Phone Model_Find X3 Lite, Phone Model_Find X3 Neo, Phone Model_Find X3 Pro, Phone Model_Find X4, Phone Model_Find X5, Phone Model_Find X6, Phone Model_Find X7, Phone Model_Find X8, Phone Model_Find X9, Phone Model_Galaxy A01, Phone Model_Galaxy A02s, Phone Model_Galaxy A10, Phone Model_Galaxy A103, Phone Model_Galaxy A11, Phone Model_Galaxy A113, Phone Model_Galaxy A12, Phone Model_Galaxy A123, Phone Model_Galaxy A12s, Phone Model_Galaxy A20, Phone Model_Galaxy A20e, Phone Model_Galaxy A21, Phone Model_Galaxy A21s, Phone Model_Galaxy A30s, Phone Model_Galaxy A31, Phone Model_Galaxy A31s, Phone Model_Galaxy A32, Phone Model_Galaxy A32 5G, Phone Model_Galaxy A40, Phone Model_Galaxy A41, Phone Model_Galaxy A42, Phone Model_Galaxy A5 (2017), Phone Model_Galaxy A50, Phone Model_Galaxy A51, Phone Model_Galaxy A52, Phone Model_Galaxy A70, Phone Model_Galaxy A71, Phone Model_Galaxy A72, Phone Model_Galaxy A73, Phone Model_Galaxy A80, Phone Model_Galaxy A83, Phone Model_Galaxy A90, Phone Model_Galaxy A93, Phone Model_Galaxy M11, Phone Model_Galaxy M21, Phone Model_Galaxy M31, ...]\n","Index: []\n","\n","[0 rows x 337 columns]\n"]}],"source":["negative_durations = df[df['Repair Duration'] <= 0]\n","print(negative_durations)"]},{"cell_type":"markdown","id":"850551ea","metadata":{"papermill":{"duration":0.012596,"end_time":"2024-06-27T17:29:04.887338","exception":false,"start_time":"2024-06-27T17:29:04.874742","status":"completed"},"tags":[]},"source":["# **4. Data Preparation for Predictive Modeling**\n","In the process of preparing our data for predictive modeling, we extract and separate the necessary variables. We assign the 'Repair Duration' column of the dataset to be our target variable that we want to predict, that will represent the number of days taken to repair the phone. The remaining columns are used as the future variables for the predictive model that we will test. These features include the phone brand, phone model, type of problem and month recieved. The date recieved and date ready columns are excluded because they are not directly useful for the model, especially in their raw date formats. The repair duration column is excluded because it is already set as a target variable."]},{"cell_type":"code","execution_count":8,"id":"6ee26c25","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.915926Z","iopub.status.busy":"2024-06-27T17:29:04.915001Z","iopub.status.idle":"2024-06-27T17:29:04.921552Z","shell.execute_reply":"2024-06-27T17:29:04.920302Z"},"papermill":{"duration":0.024058,"end_time":"2024-06-27T17:29:04.923885","exception":false,"start_time":"2024-06-27T17:29:04.899827","status":"completed"},"tags":[]},"outputs":[],"source":["y = df['Repair Duration']\n","X = df.drop(['Date Received', 'Date Ready', 'Repair Duration'], axis=1)"]},{"cell_type":"markdown","id":"7d105785","metadata":{"papermill":{"duration":0.012588,"end_time":"2024-06-27T17:29:04.949545","exception":false,"start_time":"2024-06-27T17:29:04.936957","status":"completed"},"tags":[]},"source":["#### **4.1 Visualization of the Repair Duration before model**\n","To visualize the distribution of repair durations, we use a histogram, which provides a clear view of how frequently different repair durations occur. This visualization can inform decisions about process improvements, resource allocation, and customer communication strategies by highlighting typical repair times.\n","<br><br>\n","Most frequent repair durations are between 4-5 days, and 2-3 days. As we can see in the histogram the frequency for 1-2 days and 3-4 days is low.\n","<br><br>\n","From the combined histogram of repair duration for all brands we can conclude that the phone brand Xiaomi is repaired the fastest but also has the least frequency of reparations, meanwhile iPhone and Samsung tend to be both fast with 2-2.5 days and slowest with up to 5 days for a repair. <br><br>\n","From the bar plot representing the frequency of repairs by month we can conclude that in December repairs are more frequent, followed by May. From this we can assume that people are repairing phones in the winter period before New Year the most. On the other side in April the frequency of repairs is pretty low. With this information the mobile service can schedule and organize repairs more effectively in the future, knowing that December is their busy month, meanwhile March, April and November are not that busy. The company can use this analysis to work more on promotional marketing in the months they have the least repairing work."]},{"cell_type":"code","execution_count":9,"id":"20f254ef","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:04.977432Z","iopub.status.busy":"2024-06-27T17:29:04.976539Z","iopub.status.idle":"2024-06-27T17:29:05.350641Z","shell.execute_reply":"2024-06-27T17:29:05.349607Z"},"papermill":{"duration":0.390551,"end_time":"2024-06-27T17:29:05.352845","exception":false,"start_time":"2024-06-27T17:29:04.962294","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABHw0lEQVR4nO3deVyVZf7/8fdhRxAQFdBcc981LUUt00xMxrScSjNDs2VMzb3JFtcmzEytCbUmg5Ypy6bVHfdG0dR0UnPcE0uW3EBMEOH6/eGP8+0IKODBA/e8no/H/chz3dd935/rXBhv7+UcmzHGCAAAwKLcXF0AAABAaSLsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsACVQp04dDR482NVlWN5rr72mm2++We7u7mrdurWry7lu/8s/N+vXr5fNZtP69etdXQr+BxF28D8vLi5ONptN27dvL3D9nXfeqebNm1/3cZYtW6YpU6Zc937+V6xatUrPPvusOnXqpNjYWL3yyiuF9h08eLBsNpt98fb2VsOGDTVp0iRlZmbewKpL188//+wwTk9PT1WpUkUdO3bU888/r8TERFeXqHnz5ikuLs7VZQAOPFxdAFAe7d+/X25uxfu3wrJlyxQTE0PgKaK1a9fKzc1NCxculJeX1zX7e3t7691335UkpaWl6euvv9b06dN1+PBh/fOf/yztcoukJD83BRkwYIB69eql3NxcnTlzRtu2bdPcuXP1xhtvaOHCherfv78Tqi2ZefPmqUqVKvnOYN1xxx26cOFCkeYScDbCDlAC3t7eri6h2M6fPy8/Pz9Xl1Fkqamp8vX1LfIvRw8PDz3yyCP2108//bQ6duyoTz75RLNnz1ZoaGhplVpkRfm5Kco83XLLLQ5jlaRjx46pR48eioqKUpMmTdSqVavrqlWSjDHKzMyUr6/vde/Lzc1NPj4+170foCS4jAWUwJX3XmRnZ2vq1Klq0KCBfHx8VLlyZXXu3Fnx8fGSLl9miYmJkSSHyxB5zp8/r3HjxqlmzZry9vZWo0aNNGvWLBljHI574cIFPfPMM6pSpYoqVqyoe++9V7/++qtsNpvDGaMpU6bIZrPpp59+0sMPP6xKlSqpc+fOkqQff/xRgwcP1s033ywfHx+FhYXpscce06lTpxyOlbePAwcO6JFHHlFgYKCqVq2ql156ScYYHT9+XH369FFAQIDCwsL0+uuvF+m9u3TpkqZPn6569erJ29tbderU0fPPP6+srCx7H5vNptjYWJ0/f97+XhX30ojNZlPnzp1ljNGRI0cc1i1fvly33367/Pz8VLFiRUVGRmrv3r0OfQYPHix/f38dOXJEERER8vPzU/Xq1TVt2rR88zJr1ix17NhRlStXlq+vr9q2bavPP/88X01X/tzkXULdsGGDnn76aYWEhKhGjRrFGmee2rVrKy4uThcvXtTMmTPt7XnzeKW8Y//8888O9f3pT3/SypUr1a5dO/n6+urtt9+WJMXGxqpbt24KCQmRt7e3mjZtqvnz5+cb3969e7Vhwwb7vN15552SCr9nZ/HixWrbtq18fX1VpUoVPfLII/r1118d+uTNxa+//qq+ffvK399fVatW1fjx45WTk1Oi9wv/WzizA/x/aWlpOnnyZL727Ozsa247ZcoURUdH6/HHH9dtt92m9PR0bd++XT/88IPuvvtuPfXUUzpx4oTi4+P14YcfOmxrjNG9996rdevWaejQoWrdurVWrlypCRMm6Ndff9WcOXPsfQcPHqzPPvtMgwYNUocOHbRhwwZFRkYWWtcDDzygBg0a6JVXXrH/go6Pj9eRI0c0ZMgQhYWFae/evXrnnXe0d+9ebdmyJd8vxoceekhNmjTRjBkztHTpUr388ssKDg7W22+/rW7duunVV1/VP//5T40fP1633nqr7rjjjqu+V48//rjef/99/fnPf9a4ceO0detWRUdHa9++ffryyy8lSR9++KHeeecdff/99/ZLUx07drzmPFwp7xd5pUqV7G0ffvihoqKiFBERoVdffVW///675s+fr86dO2vnzp2qU6eOvW9OTo569uypDh06aObMmVqxYoUmT56sS5cuadq0afZ+b7zxhu69914NHDhQFy9e1KJFi/TAAw9oyZIlV52fPE8//bSqVq2qSZMm6fz588UeZ57w8HDVq1fPHrJLYv/+/RowYICeeuopPfHEE2rUqJEkaf78+WrWrJnuvfdeeXh46Ntvv9XTTz+t3NxcDR8+XJI0d+5cjRw5Uv7+/nrhhRck6apn1OLi4jRkyBDdeuutio6OVkpKit544w1t2rRJO3fuVFBQkL1vTk6OIiIi1L59e82aNUurV6/W66+/rnr16mnYsGElHi/+Rxjgf1xsbKyRdNWlWbNmDtvUrl3bREVF2V+3atXKREZGXvU4w4cPNwX9lfvqq6+MJPPyyy87tP/5z382NpvNHDp0yBhjzI4dO4wkM3r0aId+gwcPNpLM5MmT7W2TJ082ksyAAQPyHe/333/P1/bJJ58YSWbjxo359vHkk0/a2y5dumRq1KhhbDabmTFjhr39zJkzxtfX1+E9KciuXbuMJPP44487tI8fP95IMmvXrrW3RUVFGT8/v6vu78q+v/32m/ntt9/MoUOHzKxZs4zNZjPNmzc3ubm5xhhjzp07Z4KCgswTTzzhsH1ycrIJDAx0aI+KijKSzMiRI+1tubm5JjIy0nh5eZnffvvN3n7le3rx4kXTvHlz061bN4f2K39u8n72OnfubC5dunTNcR49etRIMq+99lqhffr06WMkmbS0NGPM/83jlfKOffToUYf6JJkVK1bk61/Qz01ERIS5+eabHdqaNWtmunTpkq/vunXrjCSzbt06Y8zl9ygkJMQ0b97cXLhwwd5vyZIlRpKZNGmSvS1vLqZNm+awzzZt2pi2bdvmfxOAK3AZC/j/YmJiFB8fn29p2bLlNbcNCgrS3r17dfDgwWIfd9myZXJ3d9czzzzj0D5u3DgZY7R8+XJJ0ooVKyRdPgvwRyNHjix033/5y1/ytf3x/ovMzEydPHlSHTp0kCT98MMP+fo//vjj9j+7u7urXbt2MsZo6NCh9vagoCA1atQo3+WiKy1btkySNHbsWIf2cePGSZKWLl161e2v5vz586pataqqVq2q+vXra/z48erUqZO+/vpr+9mq+Ph4nT17VgMGDNDJkyfti7u7u9q3b69169bl2++IESPsf7bZbBoxYoQuXryo1atX29v/+J6eOXNGaWlpuv322wt8PwvyxBNPyN3dvaRDd+Dv7y9JOnfuXIm2r1u3riIiIvK1/3GMeWdBu3TpoiNHjigtLa3Yx9m+fbtSU1P19NNPO9zLExkZqcaNGxf4s3Dlz/Ptt99+zZ85QOIyFmB32223qV27dvnaK1WqVODlrT+aNm2a+vTpo4YNG6p58+bq2bOnBg0aVKSgdOzYMVWvXl0VK1Z0aG/SpIl9fd5/3dzcVLduXYd+9evXL3TfV/aVpNOnT2vq1KlatGiRUlNTHdYV9EurVq1aDq8DAwPl4+OjKlWq5Gu/8r6fK+WN4cqaw8LCFBQUZB9rSfj4+Ojbb7+VJP3yyy+aOXOm/SbnPHlhtFu3bgXuIyAgwOG1m5ubbr75Zoe2hg0bSpLDvS5LlizRyy+/rF27duW796goCpqnksrIyJCkfD9PRVVYLZs2bdLkyZOVkJCg33//3WFdWlqaAgMDi3WcvLnOu0z2R40bN9a///1vhzYfHx9VrVrVoa1SpUo6c+ZMsY6L/02EHcAJ7rjjDh0+fFhff/21Vq1apXfffVdz5szRggULHM6M3GgFPUXz4IMPavPmzZowYYJat24tf39/5ebmqmfPnsrNzc3Xv6AzDoWdhTBX3LhbmKKGgOJwd3dX9+7d7a8jIiLUuHFjPfXUU/rmm28kyT6+Dz/8UGFhYfn24eFR/P8lfvfdd7r33nt1xx13aN68eapWrZo8PT0VGxurjz/+uEj7cMbTTnn27NmjkJAQe3Ar7L0u7Mbegmo5fPiw7rrrLjVu3FizZ89WzZo15eXlpWXLlmnOnDkF/tw4m7POfOF/E2EHcJLg4GANGTJEQ4YMUUZGhu644w5NmTLFHnYK+6VTu3ZtrV69WufOnXP41/h///tf+/q8/+bm5uro0aNq0KCBvd+hQ4eKXOOZM2e0Zs0aTZ06VZMmTbK3l+TyW0nkjeHgwYP2M1eSlJKSorNnz9rH6gzVqlXTmDFjNHXqVG3ZskUdOnRQvXr1JEkhISEOwagwubm5OnLkiP1sjiQdOHBAkuw3Mv/rX/+Sj4+PVq5c6fBoeWxsrNPGUlQJCQk6fPiww2PpeTdnnz171uGG3+KcRfv222+VlZWlb775xuFMX0GX/YoaZPPmev/+/fnOtO3fv9+pPwsA9+wATnDl5Rt/f3/Vr1/f4ZJG3mennD171qFvr169lJOTo7feesuhfc6cObLZbLrnnnskyX4fxbx58xz6/f3vfy9ynXn/Or7yDMzcuXOLvI/r0atXrwKPN3v2bEkq0pNLxTFy5EhVqFBBM2bMkHT5PQwICNArr7xS4FN2v/32W762P86LMUZvvfWWPD09ddddd0m6/J7abDaHMyU///yzvvrqK6eO5VqOHTumwYMHy8vLSxMmTLC35wW8jRs32tvOnz+v999/v8j7LujnJi0trcBA5+fnl+9nvCDt2rVTSEiIFixY4PD3ZPny5dq3b5/Tfxbwv40zO4ATNG3aVHfeeafatm2r4OBgbd++XZ9//rnDza1t27aVJD3zzDOKiIiQu7u7+vfvr969e6tr16564YUX9PPPP6tVq1ZatWqVvv76a40ePdr+y6pt27bq16+f5s6dq1OnTtkfPc8701CUf1EHBATojjvu0MyZM5Wdna2bbrpJq1at0tGjR0vhXcmvVatWioqK0jvvvKOzZ8+qS5cu+v777/X++++rb9++6tq1q1OPV7lyZQ0ZMkTz5s3Tvn371KRJE82fP1+DBg3SLbfcov79+6tq1apKTEzU0qVL1alTJ4dw4+PjoxUrVigqKkrt27fX8uXLtXTpUj3//PP2+0ciIyM1e/Zs9ezZUw8//LBSU1MVExOj+vXr68cff3TqePL88MMP+uijj5Sbm6uzZ89q27Zt+te//iWbzaYPP/zQ4V6xHj16qFatWho6dKgmTJggd3d3vffee/ZxF0WPHj3k5eWl3r1766mnnlJGRob+8Y9/KCQkRElJSQ5927Ztq/nz5+vll19W/fr1FRISUuA9Up6ennr11Vc1ZMgQdenSRQMGDLA/el6nTh2NGTPm+t4k4I9c+SgYUBbkPYK7bdu2Atd36dLlmo+ev/zyy+a2224zQUFBxtfX1zRu3Nj87W9/MxcvXrT3uXTpkhk5cqSpWrWqsdlsDo8Dnzt3zowZM8ZUr17deHp6mgYNGpjXXnvN/sh0nvPnz5vhw4eb4OBg4+/vb/r27Wv2799vJDk8Cp73uPEfH4/O88svv5j77rvPBAUFmcDAQPPAAw+YEydOFPr4+pX7KOyR8ILep4JkZ2ebqVOnmrp16xpPT09Ts2ZNM3HiRJOZmVmk4xTkan0PHz5s3N3dHeZr3bp1JiIiwgQGBhofHx9Tr149M3jwYLN9+/Z8+zx8+LDp0aOHqVChggkNDTWTJ082OTk5DsdYuHChadCggfH29jaNGzc2sbGxBT7yXdij54X97F0p79HzvMXDw8MEBweb9u3bm4kTJ5pjx44VuN2OHTtM+/btjZeXl6lVq5aZPXt2oY+eF/YRCt98841p2bKl8fHxMXXq1DGvvvqqee+99/LtIzk52URGRpqKFSsaSfbH0K989DzPp59+atq0aWO8vb1NcHCwGThwoPnll18c+hQ2v4U9Vg9cyWZMEe8oBFAm7dq1S23atNFHH32kgQMHurocyxg8eLA+//xz+9NNAMov7tkBypELFy7ka5s7d67c3Nyu+cnFAPC/int2gHJk5syZ2rFjh7p27SoPDw8tX75cy5cv15NPPqmaNWu6ujwAKJMIO0A50rFjR8XHx2v69OnKyMhQrVq1NGXKFPv3EAEA8uOeHQAAYGncswMAACyNsAMAACyNe3Z0+SPhT5w4oYoVK5bKd/YAAADnM8bo3Llzql69utzcCj9/Q9iRdOLECZ5kAQCgnDp+/Lhq1KhR6HrCjmT/8sXjx4/bvykYAACUbenp6apZs6bDlygXhLCj//tOoYCAAMIOAADlzLVuQXH5Dcq//vqrHnnkEVWuXFm+vr5q0aKFtm/fbl9vjNGkSZNUrVo1+fr6qnv37jp48KDDPk6fPq2BAwcqICBAQUFBGjp0KB/xDgAAJLk47Jw5c0adOnWSp6enli9frp9++kmvv/66KlWqZO8zc+ZMvfnmm1qwYIG2bt0qPz8/RUREKDMz095n4MCB2rt3r+Lj47VkyRJt3LhRTz75pCuGBAAAyhiXfqjgc889p02bNum7774rcL0xRtWrV9e4ceM0fvx4SVJaWppCQ0MVFxen/v37a9++fWratKm2bdumdu3aSZJWrFihXr166ZdfflH16tWvWUd6eroCAwOVlpbGZSwAAMqJov7+dumZnW+++Ubt2rXTAw88oJCQELVp00b/+Mc/7OuPHj2q5ORkde/e3d4WGBio9u3bKyEhQZKUkJCgoKAge9CRpO7du8vNzU1bt24t8LhZWVlKT093WAAAgDW5NOwcOXJE8+fPV4MGDbRy5UoNGzZMzzzzjN5//31JUnJysiQpNDTUYbvQ0FD7uuTkZIWEhDis9/DwUHBwsL3PlaKjoxUYGGhfeOwcAADrcmnYyc3N1S233KJXXnlFbdq00ZNPPqknnnhCCxYsKNXjTpw4UWlpafbl+PHjpXo8AADgOi4NO9WqVVPTpk0d2po0aaLExERJUlhYmCQpJSXFoU9KSop9XVhYmFJTUx3WX7p0SadPn7b3uZK3t7f9MXMeNwcAwNpcGnY6deqk/fv3O7QdOHBAtWvXliTVrVtXYWFhWrNmjX19enq6tm7dqvDwcElSeHi4zp49qx07dtj7rF27Vrm5uWrfvv0NGAUAACjLXPqhgmPGjFHHjh31yiuv6MEHH9T333+vd955R++8846kyx8SNHr0aL388stq0KCB6tatq5deeknVq1dX3759JV0+E9SzZ0/75a/s7GyNGDFC/fv3L9KTWAAAwNpc+ui5JC1ZskQTJ07UwYMHVbduXY0dO1ZPPPGEfb0xRpMnT9Y777yjs2fPqnPnzpo3b54aNmxo73P69GmNGDFC3377rdzc3NSvXz+9+eab8vf3L1INPHoOAED5U9Tf3y4PO2UBYQcAgPKnXHzODgAAQGkj7AAAAEsj7AAAAEtz6dNYAABASkxM1MmTJ11dRqmpUqWKatWq5bLjE3YAAHChxMRENWrcRJkXfnd1KaXGx7eC9v93n8sCD2EHAAAXOnnypDIv/K7Kfxonz8rW+67G7FPHdWrJ6zp58iRhBwCA/2WelWvKO6y+q8uwJG5QBgAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlubSsDNlyhTZbDaHpXHjxvb1mZmZGj58uCpXrix/f3/169dPKSkpDvtITExUZGSkKlSooJCQEE2YMEGXLl260UMBAABllIerC2jWrJlWr15tf+3h8X8ljRkzRkuXLtXixYsVGBioESNG6P7779emTZskSTk5OYqMjFRYWJg2b96spKQkPfroo/L09NQrr7xyw8cCAADKHpeHHQ8PD4WFheVrT0tL08KFC/Xxxx+rW7dukqTY2Fg1adJEW7ZsUYcOHbRq1Sr99NNPWr16tUJDQ9W6dWtNnz5df/3rXzVlyhR5eXnd6OEAAIAyxuX37Bw8eFDVq1fXzTffrIEDByoxMVGStGPHDmVnZ6t79+72vo0bN1atWrWUkJAgSUpISFCLFi0UGhpq7xMREaH09HTt3bu30GNmZWUpPT3dYQEAANbk0rDTvn17xcXFacWKFZo/f76OHj2q22+/XefOnVNycrK8vLwUFBTksE1oaKiSk5MlScnJyQ5BJ2993rrCREdHKzAw0L7UrFnTuQMDAABlhksvY91zzz32P7ds2VLt27dX7dq19dlnn8nX17fUjjtx4kSNHTvW/jo9PZ3AAwCARbn8MtYfBQUFqWHDhjp06JDCwsJ08eJFnT171qFPSkqK/R6fsLCwfE9n5b0u6D6gPN7e3goICHBYAACANZWpsJORkaHDhw+rWrVqatu2rTw9PbVmzRr7+v379ysxMVHh4eGSpPDwcO3evVupqan2PvHx8QoICFDTpk1veP0AAKDscellrPHjx6t3796qXbu2Tpw4ocmTJ8vd3V0DBgxQYGCghg4dqrFjxyo4OFgBAQEaOXKkwsPD1aFDB0lSjx491LRpUw0aNEgzZ85UcnKyXnzxRQ0fPlze3t6uHBoAACgjXBp2fvnlFw0YMECnTp1S1apV1blzZ23ZskVVq1aVJM2ZM0dubm7q16+fsrKyFBERoXnz5tm3d3d315IlSzRs2DCFh4fLz89PUVFRmjZtmquGBAAAyhiXhp1FixZddb2Pj49iYmIUExNTaJ/atWtr2bJlzi4NAABYRJm6ZwcAAMDZCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSPFxdAFDWJSYm6uTJk64uo9RUqVJFtWrVcnUZAFBqCDvAVSQmJqpR4ybKvPC7q0spNT6+FbT/v/sIPAAsi7ADXMXJkyeVeeF3Vf7TOHlWrunqcpwu+9RxnVryuk6ePEnYAWBZhB2gCDwr15R3WH1XlwEAKAFuUAYAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZWZsLOjBkzZLPZNHr0aHtbZmamhg8frsqVK8vf31/9+vVTSkqKw3aJiYmKjIxUhQoVFBISogkTJujSpUs3uHoAAFBWlYmws23bNr399ttq2bKlQ/uYMWP07bffavHixdqwYYNOnDih+++/374+JydHkZGRunjxojZv3qz3339fcXFxmjRp0o0eAgAAKKNcHnYyMjI0cOBA/eMf/1ClSpXs7WlpaVq4cKFmz56tbt26qW3btoqNjdXmzZu1ZcsWSdKqVav0008/6aOPPlLr1q11zz33aPr06YqJidHFixddNSQAAFCGuDzsDB8+XJGRkerevbtD+44dO5Sdne3Q3rhxY9WqVUsJCQmSpISEBLVo0UKhoaH2PhEREUpPT9fevXsLPWZWVpbS09MdFgAAYE0erjz4okWL9MMPP2jbtm351iUnJ8vLy0tBQUEO7aGhoUpOTrb3+WPQyVuft64w0dHRmjp16nVWDwAAygOXndk5fvy4Ro0apX/+85/y8fG5oceeOHGi0tLS7Mvx48dv6PEBAMCN47Kws2PHDqWmpuqWW26Rh4eHPDw8tGHDBr355pvy8PBQaGioLl68qLNnzzpsl5KSorCwMElSWFhYvqez8l7n9SmIt7e3AgICHBYAAGBNLgs7d911l3bv3q1du3bZl3bt2mngwIH2P3t6emrNmjX2bfbv36/ExESFh4dLksLDw7V7926lpqba+8THxysgIEBNmza94WMCAABlj8vu2alYsaKaN2/u0Obn56fKlSvb24cOHaqxY8cqODhYAQEBGjlypMLDw9WhQwdJUo8ePdS0aVMNGjRIM2fOVHJysl588UUNHz5c3t7eN3xMAACg7HHpDcrXMmfOHLm5ualfv37KyspSRESE5s2bZ1/v7u6uJUuWaNiwYQoPD5efn5+ioqI0bdo0F1YNAADKkjIVdtavX+/w2sfHRzExMYqJiSl0m9q1a2vZsmWlXBkAACivXP45OwAAAKWJsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACytRGHnyJEjzq4DAACgVJQo7NSvX19du3bVRx99pMzMTGfXBAAA4DQlCjs//PCDWrZsqbFjxyosLExPPfWUvv/+e2fXBgAAcN1KFHZat26tN954QydOnNB7772npKQkde7cWc2bN9fs2bP122+/ObtOAACAErmuG5Q9PDx0//33a/HixXr11Vd16NAhjR8/XjVr1tSjjz6qpKQkZ9UJAABQItcVdrZv366nn35a1apV0+zZszV+/HgdPnxY8fHxOnHihPr06eOsOgEAAErEoyQbzZ49W7Gxsdq/f7969eqlDz74QL169ZKb2+XsVLduXcXFxalOnTrOrBUAAKDYShR25s+fr8cee0yDBw9WtWrVCuwTEhKihQsXXldxAAAA16tEYefgwYPX7OPl5aWoqKiS7B4AAMBpSnTPTmxsrBYvXpyvffHixXr//fevuygAAABnKVHYiY6OVpUqVfK1h4SE6JVXXrnuogAAAJylRGEnMTFRdevWzddeu3ZtJSYmXndRAAAAzlKisBMSEqIff/wxX/t//vMfVa5c+bqLAgAAcJYShZ0BAwbomWee0bp165STk6OcnBytXbtWo0aNUv/+/Z1dIwAAQImV6Gms6dOn6+eff9Zdd90lD4/Lu8jNzdWjjz7KPTsAAKBMKVHY8fLy0qeffqrp06frP//5j3x9fdWiRQvVrl3b2fUBAABclxKFnTwNGzZUw4YNnVULAACA05Uo7OTk5CguLk5r1qxRamqqcnNzHdavXbvWKcUBAABcrxKFnVGjRikuLk6RkZFq3ry5bDabs+sCAABwihKFnUWLFumzzz5Tr169nF0PAACAU5Xo0XMvLy/Vr1/f2bUAAAA4XYnCzrhx4/TGG2/IGOPsegAAAJyqRJex/v3vf2vdunVavny5mjVrJk9PT4f1X3zxhVOKAwAAuF4lCjtBQUG67777nF0LAACA05Uo7MTGxjq7DgAAgFJRont2JOnSpUtavXq13n77bZ07d06SdOLECWVkZDitOAAAgOtVojM7x44dU8+ePZWYmKisrCzdfffdqlixol599VVlZWVpwYIFzq4TAACgREp0ZmfUqFFq166dzpw5I19fX3v7fffdpzVr1jitOAAAgOtVojM73333nTZv3iwvLy+H9jp16ujXX391SmEAAADOUKIzO7m5ucrJycnX/ssvv6hixYrXXRQAAICzlCjs9OjRQ3PnzrW/ttlsysjI0OTJk/kKCQAAUKaU6DLW66+/roiICDVt2lSZmZl6+OGHdfDgQVWpUkWffPKJs2sEAAAosRKd2alRo4b+85//6Pnnn9eYMWPUpk0bzZgxQzt37lRISEiR9zN//ny1bNlSAQEBCggIUHh4uJYvX25fn5mZqeHDh6ty5cry9/dXv379lJKS4rCPxMRERUZGqkKFCgoJCdGECRN06dKlkgwLAABYUInO7EiSh4eHHnnkkes6eI0aNTRjxgw1aNBAxhi9//776tOnj3bu3KlmzZppzJgxWrp0qRYvXqzAwECNGDFC999/vzZt2iRJysnJUWRkpMLCwrR582YlJSXp0Ucflaenp1555ZXrqg0AAFhDicLOBx98cNX1jz76aJH207t3b4fXf/vb3zR//nxt2bJFNWrU0MKFC/Xxxx+rW7duki5/cnOTJk20ZcsWdejQQatWrdJPP/2k1atXKzQ0VK1bt9b06dP117/+VVOmTMn3tBgAAPjfU6KwM2rUKIfX2dnZ+v333+Xl5aUKFSoUOez8UU5OjhYvXqzz588rPDxcO3bsUHZ2trp3727v07hxY9WqVUsJCQnq0KGDEhIS1KJFC4WGhtr7REREaNiwYdq7d6/atGlTkuEBAAALKVHYOXPmTL62gwcPatiwYZowYUKx9rV7926Fh4crMzNT/v7++vLLL9W0aVPt2rVLXl5eCgoKcugfGhqq5ORkSVJycrJD0Mlbn7euMFlZWcrKyrK/Tk9PL1bNAACg/Cjxd2NdqUGDBpoxY0a+sz7X0qhRI+3atUtbt27VsGHDFBUVpZ9++slZZRUoOjpagYGB9qVmzZqlejwAAOA6Tgs70uWblk+cOFGsbby8vFS/fn21bdtW0dHRatWqld544w2FhYXp4sWLOnv2rEP/lJQUhYWFSZLCwsLyPZ2V9zqvT0EmTpyotLQ0+3L8+PFi1QwAAMqPEl3G+uabbxxeG2OUlJSkt956S506dbqugnJzc5WVlaW2bdvK09NTa9asUb9+/SRJ+/fvV2JiosLDwyVJ4eHh+tvf/qbU1FT7I+/x8fEKCAhQ06ZNCz2Gt7e3vL29r6tOAABQPpQo7PTt29fhtc1mU9WqVdWtWze9/vrrRd7PxIkTdc8996hWrVo6d+6cPv74Y61fv14rV65UYGCghg4dqrFjxyo4OFgBAQEaOXKkwsPD1aFDB0mXP8m5adOmGjRokGbOnKnk5GS9+OKLGj58OGEGAABIKmHYyc3NdcrBU1NT9eijjyopKUmBgYFq2bKlVq5cqbvvvluSNGfOHLm5ualfv37KyspSRESE5s2bZ9/e3d1dS5Ys0bBhwxQeHi4/Pz9FRUVp2rRpTqkPAACUfyX+UEFnWLhw4VXX+/j4KCYmRjExMYX2qV27tpYtW+bs0gAAgEWUKOyMHTu2yH1nz55dkkMAAAA4RYnCzs6dO7Vz505lZ2erUaNGkqQDBw7I3d1dt9xyi72fzWZzTpUAAAAlVKKw07t3b1WsWFHvv/++KlWqJOnyBw0OGTJEt99+u8aNG+fUIgEAAEqqRJ+z8/rrrys6OtoedCSpUqVKevnll4v1NBYAAEBpK1HYSU9P12+//Zav/bffftO5c+euuygAAABnKVHYue+++zRkyBB98cUX+uWXX/TLL7/oX//6l4YOHar777/f2TUCAACUWInu2VmwYIHGjx+vhx9+WNnZ2Zd35OGhoUOH6rXXXnNqgQAAANejRGGnQoUKmjdvnl577TUdPnxYklSvXj35+fk5tTgAAIDrdV1fBJqUlKSkpCQ1aNBAfn5+MsY4qy4AAACnKFHYOXXqlO666y41bNhQvXr1UlJSkiRp6NChPHYOAADKlBKFnTFjxsjT01OJiYmqUKGCvf2hhx7SihUrnFYcAADA9SrRPTurVq3SypUrVaNGDYf2Bg0a6NixY04pDAAAwBlKdGbn/PnzDmd08pw+fVre3t7XXRQAAICzlCjs3H777frggw/sr202m3JzczVz5kx17drVacUBAABcrxJdxpo5c6buuusubd++XRcvXtSzzz6rvXv36vTp09q0aZOzawQAACixEp3Zad68uQ4cOKDOnTurT58+On/+vO6//37t3LlT9erVc3aNAAAAJVbsMzvZ2dnq2bOnFixYoBdeeKE0agIAAHCaYp/Z8fT01I8//lgatQAAADhdiS5jPfLII1q4cKGzawEAAHC6Et2gfOnSJb333ntavXq12rZtm+87sWbPnu2U4gAAAK5XscLOkSNHVKdOHe3Zs0e33HKLJOnAgQMOfWw2m/OqAwAAuE7FCjsNGjRQUlKS1q1bJ+ny10O8+eabCg0NLZXiAAAArlex7tm58lvNly9frvPnzzu1IAAAAGcq0Q3Kea4MPwAAAGVNscKOzWbLd08O9+gAAICyrFj37BhjNHjwYPuXfWZmZuovf/lLvqexvvjiC+dVCAAAcB2KFXaioqIcXj/yyCNOLQYAAMDZihV2YmNjS6sOAACAUnFdNygDAACUdYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaS4NO9HR0br11ltVsWJFhYSEqG/fvtq/f79Dn8zMTA0fPlyVK1eWv7+/+vXrp5SUFIc+iYmJioyMVIUKFRQSEqIJEybo0qVLN3IoAACgjHJp2NmwYYOGDx+uLVu2KD4+XtnZ2erRo4fOnz9v7zNmzBh9++23Wrx4sTZs2KATJ07o/vvvt6/PyclRZGSkLl68qM2bN+v9999XXFycJk2a5IohAQCAMsbDlQdfsWKFw+u4uDiFhIRox44duuOOO5SWlqaFCxfq448/Vrdu3SRJsbGxatKkibZs2aIOHTpo1apV+umnn7R69WqFhoaqdevWmj59uv76179qypQp8vLycsXQAABAGVGm7tlJS0uTJAUHB0uSduzYoezsbHXv3t3ep3HjxqpVq5YSEhIkSQkJCWrRooVCQ0PtfSIiIpSenq69e/cWeJysrCylp6c7LAAAwJrKTNjJzc3V6NGj1alTJzVv3lySlJycLC8vLwUFBTn0DQ0NVXJysr3PH4NO3vq8dQWJjo5WYGCgfalZs6aTRwMAAMqKMhN2hg8frj179mjRokWlfqyJEycqLS3Nvhw/frzUjwkAAFzDpffs5BkxYoSWLFmijRs3qkaNGvb2sLAwXbx4UWfPnnU4u5OSkqKwsDB7n++//95hf3lPa+X1uZK3t7e8vb2dPAoAAFAWufTMjjFGI0aM0Jdffqm1a9eqbt26Duvbtm0rT09PrVmzxt62f/9+JSYmKjw8XJIUHh6u3bt3KzU11d4nPj5eAQEBatq06Y0ZCAAAKLNcemZn+PDh+vjjj/X111+rYsWK9ntsAgMD5evrq8DAQA0dOlRjx45VcHCwAgICNHLkSIWHh6tDhw6SpB49eqhp06YaNGiQZs6cqeTkZL344osaPnw4Z28AAIBrw878+fMlSXfeeadDe2xsrAYPHixJmjNnjtzc3NSvXz9lZWUpIiJC8+bNs/d1d3fXkiVLNGzYMIWHh8vPz09RUVGaNm3ajRoGAAAow1wadowx1+zj4+OjmJgYxcTEFNqndu3aWrZsmTNLAwAAFlFmnsYCAAAoDYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaS4NOxs3blTv3r1VvXp12Ww2ffXVVw7rjTGaNGmSqlWrJl9fX3Xv3l0HDx506HP69GkNHDhQAQEBCgoK0tChQ5WRkXEDRwEAAMoyl4ad8+fPq1WrVoqJiSlw/cyZM/Xmm29qwYIF2rp1q/z8/BQREaHMzEx7n4EDB2rv3r2Kj4/XkiVLtHHjRj355JM3aggAAKCM83Dlwe+55x7dc889Ba4zxmju3Ll68cUX1adPH0nSBx98oNDQUH311Vfq37+/9u3bpxUrVmjbtm1q166dJOnvf/+7evXqpVmzZql69eo3bCwAAKBsKrP37Bw9elTJycnq3r27vS0wMFDt27dXQkKCJCkhIUFBQUH2oCNJ3bt3l5ubm7Zu3VrovrOyspSenu6wAAAAayqzYSc5OVmSFBoa6tAeGhpqX5ecnKyQkBCH9R4eHgoODrb3KUh0dLQCAwPtS82aNZ1cPQAAKCvKbNgpTRMnTlRaWpp9OX78uKtLAgAApaTMhp2wsDBJUkpKikN7SkqKfV1YWJhSU1Md1l+6dEmnT5+29ymIt7e3AgICHBYAAGBNZTbs1K1bV2FhYVqzZo29LT09XVu3blV4eLgkKTw8XGfPntWOHTvsfdauXavc3Fy1b9/+htcMAADKHpc+jZWRkaFDhw7ZXx89elS7du1ScHCwatWqpdGjR+vll19WgwYNVLduXb300kuqXr26+vbtK0lq0qSJevbsqSeeeEILFixQdna2RowYof79+/MkFgAAkOTisLN9+3Z17drV/nrs2LGSpKioKMXFxenZZ5/V+fPn9eSTT+rs2bPq3LmzVqxYIR8fH/s2//znPzVixAjdddddcnNzU79+/fTmm2/e8LEAAICyyaVh584775QxptD1NptN06ZN07Rp0wrtExwcrI8//rg0ygMAABZQZu/ZAQAAcAbCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDSXfhEoAADXkpiYqJMnT7q6jFKzb98+V5dgeYQdAECZlZiYqEaNmyjzwu+uLgXlGGEHAFBmnTx5UpkXflflP42TZ+Wari6nVFw4sl1p333k6jIsjbADACjzPCvXlHdYfVeXUSqyTx13dQmWxw3KAADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0iwTdmJiYlSnTh35+Pioffv2+v77711dEgAAKAMsEXY+/fRTjR07VpMnT9YPP/ygVq1aKSIiQqmpqa4uDQAAuJglws7s2bP1xBNPaMiQIWratKkWLFigChUq6L333nN1aQAAwMU8XF3A9bp48aJ27NihiRMn2tvc3NzUvXt3JSQkuLCyyxITE3Xy5ElXl1GqqlSpolq1arm6DAAAClTuw87JkyeVk5Oj0NBQh/bQ0FD997//LXCbrKwsZWVl2V+npaVJktLT051a2/Hjx9W23a3Kyrzg1P2WNV7ePvroww/yzYEV7N+/X5KUlXxIuRczXVyN82Wf/kWStGPHDmVkZLi4mtLj5uam3NxcV5dRaqw8Pqv/HZSk7FPHJVl3jHn/n8nIyHD679m8/Rljrt7RlHO//vqrkWQ2b97s0D5hwgRz2223FbjN5MmTjSQWFhYWFhYWCyzHjx+/alYo92d2qlSpInd3d6WkpDi0p6SkKCwsrMBtJk6cqLFjx9pf5+bm6vTp06pcubJsNpvTaktPT1fNmjV1/PhxBQQEOG2/ZYnVx8j4yj+rj5HxlX9WH2Npjs8Yo3Pnzql69epX7Vfuw46Xl5fatm2rNWvWqG/fvpIuh5c1a9ZoxIgRBW7j7e0tb29vh7agoKBSqzEgIMCSP8B/ZPUxMr7yz+pjZHzln9XHWFrjCwwMvGafch92JGns2LGKiopSu3btdNttt2nu3Lk6f/68hgwZ4urSAACAi1ki7Dz00EP67bffNGnSJCUnJ6t169ZasWKFJW+YBQAAxWOJsCNJI0aMKPSylat4e3tr8uTJ+S6ZWYnVx8j4yj+rj5HxlX9WH2NZGJ/NmGs9rwUAAFB+WeITlAEAAApD2AEAAJZG2AEAAJZG2AEAAJZG2LkOGzduVO/evVW9enXZbDZ99dVX19xm/fr1uuWWW+Tt7a369esrLi6u1OssqeKOb/369bLZbPmW5OTkG1NwMUVHR+vWW29VxYoVFRISor59+9q/h+dqFi9erMaNG8vHx0ctWrTQsmXLbkC1xVeS8cXFxeWbPx8fnxtUcfHNnz9fLVu2tH9YWXh4uJYvX37VbcrL/EnFH195m78rzZgxQzabTaNHj75qv/I0h39UlPGVtzmcMmVKvnobN2581W1cMX+Enetw/vx5tWrVSjExMUXqf/ToUUVGRqpr167atWuXRo8erccff1wrV64s5UpLprjjy7N//34lJSXZl5CQkFKq8Pps2LBBw4cP15YtWxQfH6/s7Gz16NFD58+fL3SbzZs3a8CAARo6dKh27typvn37qm/fvtqzZ88NrLxoSjI+6fKnnP5x/o4dO3aDKi6+GjVqaMaMGdqxY4e2b9+ubt26qU+fPtq7d2+B/cvT/EnFH59Uvubvj7Zt26a3335bLVu2vGq/8jaHeYo6Pqn8zWGzZs0c6v33v/9daF+XzZ9zvo4TksyXX3551T7PPvusadasmUPbQw89ZCIiIkqxMucoyvjWrVtnJJkzZ87ckJqcLTU11UgyGzZsKLTPgw8+aCIjIx3a2rdvb5566qnSLu+6FWV8sbGxJjAw8MYVVQoqVapk3n333QLXlef5y3O18ZXX+Tt37pxp0KCBiY+PN126dDGjRo0qtG95nMPijK+8zeHkyZNNq1atitzfVfPHmZ0bKCEhQd27d3doi4iIUEJCgosqKh2tW7dWtWrVdPfdd2vTpk2uLqfI0tLSJEnBwcGF9inPc1iU8UlSRkaGateurZo1a17zLEJZkpOTo0WLFun8+fMKDw8vsE95nr+ijE8qn/M3fPhwRUZG5pubgpTHOSzO+KTyN4cHDx5U9erVdfPNN2vgwIFKTEwstK+r5s8yn6BcHiQnJ+f7CovQ0FClp6frwoUL8vX1dVFlzlGtWjUtWLBA7dq1U1ZWlt59913deeed2rp1q2655RZXl3dVubm5Gj16tDp16qTmzZsX2q+wOSyr9yXlKer4GjVqpPfee08tW7ZUWlqaZs2apY4dO2rv3r2qUaPGDay46Hbv3q3w8HBlZmbK399fX375pZo2bVpg3/I4f8UZX3mcv0WLFumHH37Qtm3bitS/vM1hccdX3uawffv2iouLU6NGjZSUlKSpU6fq9ttv1549e1SxYsV8/V01f4QdOE2jRo3UqFEj++uOHTvq8OHDmjNnjj788EMXVnZtw4cP1549e656rbk8K+r4wsPDHc4adOzYUU2aNNHbb7+t6dOnl3aZJdKoUSPt2rVLaWlp+vzzzxUVFaUNGzYUGgjKm+KMr7zN3/HjxzVq1CjFx8eX6ZtwS6ok4ytvc3jPPffY/9yyZUu1b99etWvX1meffaahQ4e6sDJHhJ0bKCwsTCkpKQ5tKSkpCggIKPdndQpz2223lfkAMWLECC1ZskQbN2685r+cCpvDsLCw0izxuhRnfFfy9PRUmzZtdOjQoVKq7vp5eXmpfv36kqS2bdtq27ZteuONN/T222/n61se568447tSWZ+/HTt2KDU11eHMb05OjjZu3Ki33npLWVlZcnd3d9imPM1hScZ3pbI+h1cKCgpSw4YNC63XVfPHPTs3UHh4uNasWePQFh8ff9Xr7+Xdrl27VK1aNVeXUSBjjEaMGKEvv/xSa9euVd26da+5TXmaw5KM70o5OTnavXt3mZ3DguTm5iorK6vAdeVp/gpztfFdqazP31133aXdu3dr165d9qVdu3YaOHCgdu3aVWAQKE9zWJLxXamsz+GVMjIydPjw4ULrddn8lertzxZ37tw5s3PnTrNz504jycyePdvs3LnTHDt2zBhjzHPPPWcGDRpk73/kyBFToUIFM2HCBLNv3z4TExNj3N3dzYoVK1w1hKsq7vjmzJljvvrqK3Pw4EGze/duM2rUKOPm5mZWr17tqiFc1bBhw0xgYKBZv369SUpKsi+///67vc+gQYPMc889Z3+9adMm4+HhYWbNmmX27dtnJk+ebDw9Pc3u3btdMYSrKsn4pk6dalauXGkOHz5sduzYYfr37298fHzM3r17XTGEa3ruuefMhg0bzNGjR82PP/5onnvuOWOz2cyqVauMMeV7/owp/vjK2/wV5Mqnlcr7HF7pWuMrb3M4btw4s379enP06FGzadMm0717d1OlShWTmppqjCk780fYuQ55j1pfuURFRRljjImKijJdunTJt03r1q2Nl5eXufnmm01sbOwNr7uoiju+V1991dSrV8/4+PiY4OBgc+edd5q1a9e6pvgiKGhskhzmpEuXLvbx5vnss89Mw4YNjZeXl2nWrJlZunTpjS28iEoyvtGjR5tatWoZLy8vExoaanr16mV++OGHG198ET322GOmdu3axsvLy1StWtXcdddd9iBgTPmeP2OKP77yNn8FuTIMlPc5vNK1xlfe5vChhx4y1apVM15eXuamm24yDz30kDl06JB9fVmZP5sxxpTuuSMAAADX4Z4dAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdACVy5513avTo0a4uw6ni4uIUFBR0Q461f/9+hYWF6dy5c2WiHklasGCBevfufcOOB9wohB2gHBk8eLBsNptsNps8PT1Vt25dPfvss8rMzLzhtXzxxRcl+hbmK8cQGhqqu+++W++9955yc3NLodKC1alTR3PnznVoe+ihh3TgwIEbcvyJEydq5MiRqlix4g05XlE89thj+uGHH/Tdd9+5uhTAqQg7QDnTs2dPJSUl6ciRI5ozZ47efvttTZ48+YbXERwcfNVf1BcvXix0Xd4Yfv75Zy1fvlxdu3bVqFGj9Kc//UmXLl0qcU3GmOva3tfXVyEhISXevqgSExO1ZMkSDR48uNSPVRxeXl56+OGH9eabb7q6FMCpCDtAOePt7a2wsDDVrFlTffv2Vffu3RUfH29fn5ubq+joaNWtW1e+vr5q1aqVPv/8c/v69evXy2azaenSpWrZsqV8fHzUoUMH7dmzx97n1KlTGjBggG666SZVqFBBLVq00CeffOJQx5WXserUqaPp06fr0UcfVUBAgJ588slrjuGmm27SLbfcoueff15ff/21li9frri4OEnSzz//LJvNpl27dtm3O3v2rGw2m9avX+8wluXLl6tt27by9vbWv//9bx0+fFh9+vRRaGio/P39deutt2r16tUOtR87dkxjxoyxn2WSCr5sNH/+fNWrV09eXl5q1KiRPvzwQ4f1NptN7777ru677z5VqFBBDRo00DfffFPo2CXps88+U6tWrXTTTTc5tMfFxalWrVqqUKGC7rvvPp06dcph/bXGNW3aNDVv3jzf8Vq3bq2XXnrJ/p7ddttt8vPzU1BQkDp16qRjx47Z+/bu3VvffPONLly4cNUxAOUJYQcox/bs2aPNmzfLy8vL3hYdHa0PPvhACxYs0N69ezVmzBg98sgj2rBhg8O2EyZM0Ouvv65t27apatWq6t27t7KzsyVJmZmZatu2rZYuXao9e/boySef1KBBg/T9999ftZ5Zs2apVatW2rlzp/2Xa1F169ZNrVq10hdffFGs7STpueee04wZM7Rv3z61bNlSGRkZ6tWrl9asWaOdO3eqZ8+e6t27txITEyVdvgRXo0YNTZs2TUlJSUpKSipwv19++aVGjRqlcePGac+ePXrqqac0ZMgQrVu3zqHf1KlT9eCDD+rHH39Ur169NHDgQJ0+fbrQer/77ju1a9fOoW3r1q0aOnSoRowYoV27dqlr1656+eWXHfpca1yPPfaY9u3bp23bttm32blzp3788UcNGTJEly5dUt++fdWlSxf9+OOPSkhI0JNPPmkPe5LUrl07Xbp0SVu3bi3COw+UE6X+VaMAnCYqKsq4u7sbPz8/4+3tbSQZNzc38/nnnxtjjMnMzDQVKlQwmzdvdthu6NChZsCAAcaY//s2+0WLFtnXnzp1yvj6+ppPP/200GNHRkaacePG2V9f+e3NtWvXNn379i3SGPr06VPguoceesg0adLEGGPM0aNHjSSzc+dO+/ozZ84YSWbdunUOY/nqq6+uedxmzZqZv//97w71zpkzx6FPbGysCQwMtL/u2LGjeeKJJxz6PPDAA6ZXr17215LMiy++aH+dkZFhJJnly5cXWkurVq3MtGnTHNoGDBjgsF9jLr8ff6ynKOO65557zLBhw+yvR44cae68805jzOV5lmTWr19/1X1WqlTJxMXFXbUPUJ5wZgcoZ7p27apdu3Zp69atioqK0pAhQ9SvXz9J0qFDh/T777/r7rvvlr+/v3354IMPdPjwYYf9hIeH2/8cHBysRo0aad++fZKknJwcTZ8+XS1atFBwcLD8/f21cuVK+xmEwlx5tqK4jDEOZxmK6srjZmRkaPz48WrSpImCgoLk7++vffv2XbP+K+3bt0+dOnVyaOvUqZP9fcrTsmVL+5/9/PwUEBCg1NTUQvd74cIF+fj45DtW+/btHdr+OEdFHdcTTzyhTz75RJmZmbp48aI+/vhjPfbYY5Iuz/PgwYMVERGh3r1764033ijwrJavr69+//33QusHyhsPVxcAoHj8/PxUv359SdJ7772nVq1aaeHChRo6dKgyMjIkSUuXLs13P4i3t3eRj/Haa6/pjTfe0Ny5c9WiRQv5+flp9OjRV73pOK+267Fv3z7VrVtXkuTmdvnfYsYY+/q8y2zXOu748eMVHx+vWbNmqX79+vL19dWf//zna9ZfUp6eng6vbTbbVZ8sq1Klis6cOVPs4xRlXL1795a3t7e+/PJLeXl5KTs7W3/+85/t62NjY/XMM89oxYoV+vTTT/Xiiy8qPj5eHTp0sPc5ffq0qlatWuz6gLKKsAOUY25ubnr++ec1duxYPfzww2ratKm8vb2VmJioLl26XHXbLVu2qFatWpKkM2fO6MCBA2rSpIkkadOmTerTp48eeeQRSZdvej5w4ICaNm1aamNZu3atdu/erTFjxkiS/ZdtUlKS2rRpI0kONytfzaZNmzR48GDdd999ki6fEfn5558d+nh5eSknJ+eq+2nSpIk2bdqkqKgoh31f7/vQpk0b/fTTT/mOdeV9Mlu2bHF4XZRxeXh4KCoqSrGxsfLy8lL//v3l6+ub7/ht2rTRxIkTFR4ero8//tgedg4fPqzMzEz7ew5YAWEHKOceeOABTZgwQTExMRo/frzGjx+vMWPGKDc3V507d1ZaWpo2bdqkgIAAh1/a06ZNU+XKlRUaGqoXXnhBVapUUd++fSVJDRo00Oeff67NmzerUqVKmj17tlJSUpwWdrKyspScnKycnBylpKRoxYoVio6O1p/+9Cc9+uijki5fSunQoYNmzJihunXrKjU1VS+++GKR9t+gQQN98cUX6t27t2w2m1566aV8Z1rq1KmjjRs3qn///vL29laVKlXy7WfChAl68MEH1aZNG3Xv3l3ffvutvvjiC4cnoEoiIiJCjz/+uHJycuTu7i5JeuaZZ9SpUyfNmjVLffr00cqVK7VixYpij0uSHn/8cYfgmufo0aN65513dO+996p69erav3+/Dh48aH/Ppcs3T998882qV6/edY0RKFNcfdMQgKIr7Obe6OhoU7VqVZORkWFyc3PN3LlzTaNGjYynp6epWrWqiYiIMBs2bDDG/N9Nvd9++61p1qyZ8fLyMrfddpv5z3/+Y9/fqVOnTJ8+fYy/v78JCQkxL774onn00Ucdjl3QDcpX3vBb2BgkGUnGw8PDVK1a1XTv3t289957Jicnx6HvTz/9ZMLDw42vr69p3bq1WbVqVYE3KJ85c8Zhu6NHj5quXbsaX19fU7NmTfPWW2/lqzchIcG0bNnSfqO3MflvUDbGmHnz5pmbb77ZeHp6moYNG5oPPvjAYb0k8+WXXzq0BQYGmtjY2ELfg+zsbFO9enWzYsUKh/aFCxeaGjVqGF9fX9O7d28za9Ysh3qKMq48t99+u2nWrJlDW3Jysunbt6+pVq2a8fLyMrVr1zaTJk1yeN979OhhoqOjC60dKI9sxvzhgjgAy1u/fr26du2qM2fO3NCvIoCjmJgYffPNN1q5cqXT922MUYMGDfT0009r7NixRd5u79696tatmw4cOKDAwECn1wW4CpexAMAFnnrqKZ09e1bnzp1z6ldG/Pbbb1q0aJGSk5M1ZMiQYm2blJSkDz74gKADyyHsAIALeHh46IUXXnD6fkNCQlSlShW98847qlSpUrG27d69u9PrAcoCLmMBAABL40MFAQCApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApf0/NxmYe35PChQAAAAASUVORK5CYII=","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","plt.hist(df['Repair Duration'], bins=8, edgecolor='black')\n","plt.title('Histogram of Repair Duration')\n","plt.xlabel('Repair Duration (days)')\n","plt.ylabel('Frequency')\n","plt.show()"]},{"cell_type":"code","execution_count":10,"id":"f6a0542d","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:05.381283Z","iopub.status.busy":"2024-06-27T17:29:05.380938Z","iopub.status.idle":"2024-06-27T17:29:05.825666Z","shell.execute_reply":"2024-06-27T17:29:05.824696Z"},"papermill":{"duration":0.461941,"end_time":"2024-06-27T17:29:05.828286","exception":false,"start_time":"2024-06-27T17:29:05.366345","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Create a new DataFrame that includes the brand information\n","brand_columns = [col for col in df.columns if 'Phone Brand_' in col]\n","\n","# Melt the DataFrame to have a long form df\n","df_melted = df.melt(id_vars=['Repair Duration'], \n","                    value_vars=brand_columns, \n","                    var_name='Brand', \n","                    value_name='Presence')\n","\n","# Filter the DataFrame to only include rows where the brand is present\n","df_melted = df_melted[df_melted['Presence'] == 1]\n","\n","# Extract the brand names\n","df_melted['Brand'] = df_melted['Brand'].str.replace('Phone Brand_', '')\n","\n","plt.figure(figsize=(10, 6))\n","\n","brands = df_melted['Brand'].unique()\n","for brand in brands:\n","    plt.hist(df_melted[df_melted['Brand'] == brand]['Repair Duration'], \n","             bins=5, alpha=0.5, label=brand, edgecolor='black')\n","\n","plt.title('Combined Histogram of Repair Duration for All Brands')\n","plt.xlabel('Repair Duration (days)')\n","plt.ylabel('Frequency')\n","plt.legend(title='Phone Brand')\n","plt.show()"]},{"cell_type":"code","execution_count":11,"id":"34388fe1","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:05.858643Z","iopub.status.busy":"2024-06-27T17:29:05.85828Z","iopub.status.idle":"2024-06-27T17:29:08.324664Z","shell.execute_reply":"2024-06-27T17:29:08.323578Z"},"papermill":{"duration":2.484084,"end_time":"2024-06-27T17:29:08.327059","exception":false,"start_time":"2024-06-27T17:29:05.842975","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1400x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import warnings\n","warnings.filterwarnings('ignore', category=FutureWarning, module='seaborn')\n","import seaborn as sns\n","\n","# Create bins for the repair durations\n","df_melted['Repair Duration Bin'] = pd.cut(df_melted['Repair Duration'], bins=range(0, max(df_melted['Repair Duration']) + 2, 2))\n","\n","plt.figure(figsize=(14, 8))\n","\n","sns.countplot(data=df_melted, x='Repair Duration Bin', hue='Brand', palette='Set1')\n","\n","plt.title('Bar Plot of Repair Duration for All Brands')\n","plt.xlabel('Repair Duration (days)')\n","plt.ylabel('Frequency')\n","plt.legend(title='Phone Brand')\n","\n","plt.show()"]},{"cell_type":"code","execution_count":12,"id":"9fbd28f6","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:08.357018Z","iopub.status.busy":"2024-06-27T17:29:08.356266Z","iopub.status.idle":"2024-06-27T17:29:08.658874Z","shell.execute_reply":"2024-06-27T17:29:08.657838Z"},"papermill":{"duration":0.320019,"end_time":"2024-06-27T17:29:08.661141","exception":false,"start_time":"2024-06-27T17:29:08.341122","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["repairs_per_month = df['Month Received'].value_counts().sort_index()\n","\n","#  bar plot\n","plt.figure(figsize=(10, 6))\n","plt.bar(repairs_per_month.index, repairs_per_month.values)\n","plt.title('BarPlot Frequency of Repairs by Month')\n","plt.xlabel('Month')\n","plt.ylabel('Number of Repairs')\n","plt.xticks(ticks=range(1, 13), labels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n","plt.grid(axis='y')\n","\n","plt.show()\n"]},{"cell_type":"markdown","id":"fc84d35f","metadata":{"papermill":{"duration":0.014838,"end_time":"2024-06-27T17:29:08.691611","exception":false,"start_time":"2024-06-27T17:29:08.676773","status":"completed"},"tags":[]},"source":["## **5. Model Training and Evaluation Setup**\n","Using the sk-learn library, we facilitate the training and evaluation of our predictive model. Initially, we preprocess the dataset by excluding non-predictive columns such as 'Repair Duration', 'Date Received', and 'Date Ready' from our feature set (X). These columns are not directly involved in predicting repair durations but are crucial for understanding temporal aspects and ensuring data integrity. To effectively train and evaluate our model, we split the dataset into training and testing subsets using the train_test_split function from sklearn library, with 75% used for training and 25% for testing. This approach enables us to assess the model's performance, enhancing its reliability and generalizability. To maintain the distribution of repair durations across training and testing sets, we employ the stratify parameter in train_test_split. This ensures that both subsets contain proportional representations of repair durations, thereby preventing bias in model training.For debugging we set a random_state of 42 for reproducibility, ensuring consistent results in subsequent runs of our model training and evaluation pipeline, and after debugging we removed that parameter."]},{"cell_type":"code","execution_count":13,"id":"c3bb4606","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:08.722923Z","iopub.status.busy":"2024-06-27T17:29:08.722128Z","iopub.status.idle":"2024-06-27T17:29:09.193643Z","shell.execute_reply":"2024-06-27T17:29:09.192731Z"},"papermill":{"duration":0.489706,"end_time":"2024-06-27T17:29:09.196269","exception":false,"start_time":"2024-06-27T17:29:08.706563","status":"completed"},"tags":[]},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y,stratify=y)"]},{"cell_type":"markdown","id":"23afb2fc","metadata":{"papermill":{"duration":0.013804,"end_time":"2024-06-27T17:29:09.225318","exception":false,"start_time":"2024-06-27T17:29:09.211514","status":"completed"},"tags":[]},"source":["## **6. Comparative Analysis of Machine Learning Classifiers for Predicting Repair Durations**\n","These steps collectively demonstrate the application of different learning classifiers to predict repair durations based on smartphone service data. Each classifier offers a unique approach to learning from the data and making predictions, from neural networks (MLPClassifier), linear models (LogisticRegression), to tree-based models (DecisionTreeClassifier). By comparing their accuracies and performance metrics such as precision, recall, and F1-score from the classification report, we can analyze which model best suits the dataset and problem domain.\n","<br><br>\n","First we tried with the MLP Classifier and evaluated the model's performance using metrics such as accuracy and classification report. The accuracy was ranging from 0.74 to 0.80 which is the proportion of correctly predicted instances out of total number of instances in the test set. We also tested for Precision, Recall, F1-score, and Support for each class. Precision is representing the correct predictions for each class, Recall is the true positives correctlu predicted, F1-Score is the harmonic mean of precision and recall, and Support is the number of occurences of each class."]},{"cell_type":"code","execution_count":14,"id":"1e5d27d3","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:09.255363Z","iopub.status.busy":"2024-06-27T17:29:09.254342Z","iopub.status.idle":"2024-06-27T17:29:30.425032Z","shell.execute_reply":"2024-06-27T17:29:30.423215Z"},"papermill":{"duration":21.18933,"end_time":"2024-06-27T17:29:30.428706","exception":false,"start_time":"2024-06-27T17:29:09.239376","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0.8043478260869565\n","              precision    recall  f1-score   support\n","\n","           1       1.00      1.00      1.00         1\n","           2       0.81      0.82      0.81       112\n","           4       0.59      0.59      0.59        17\n","           5       0.83      0.82      0.82       146\n","\n","    accuracy                           0.80       276\n","   macro avg       0.81      0.81      0.81       276\n","weighted avg       0.80      0.80      0.80       276\n","\n"]}],"source":["from sklearn.metrics import classification_report\n","from sklearn.neural_network import MLPClassifier\n","mlpc = MLPClassifier(max_iter=10000, activation=\"tanh\", solver=\"lbfgs\")\n","mlpc.fit(X_train, y_train)\n","y_pred=mlpc.predict(X_test)\n","\n","print(mlpc.score(X_test,y_test))\n","# Precision for correct predictions for each class\n","# Recall of true positives correctly predicted\n","# F1-Score harmonic mean of precision and recall\n","# Support, the number of occurences of each class\n","print(classification_report(y_test, y_pred))"]},{"cell_type":"code","execution_count":15,"id":"dc3e2649","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:30.538234Z","iopub.status.busy":"2024-06-27T17:29:30.537875Z","iopub.status.idle":"2024-06-27T17:29:30.98383Z","shell.execute_reply":"2024-06-27T17:29:30.982756Z"},"papermill":{"duration":0.489395,"end_time":"2024-06-27T17:29:30.986094","exception":false,"start_time":"2024-06-27T17:29:30.496699","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6485507246376812\n"]}],"source":["from sklearn.svm import SVC\n","\n","svc = SVC()\n","svc.fit(X_train, y_train)\n","y_pred = svc.predict(X_test)\n","print(svc.score(X_test, y_test))"]},{"cell_type":"markdown","id":"3a7deae2","metadata":{"papermill":{"duration":0.015103,"end_time":"2024-06-27T17:29:31.01676","exception":false,"start_time":"2024-06-27T17:29:31.001657","status":"completed"},"tags":[]},"source":["#### **6.1 Trying Logistic Regression model**\n","The nature of the project is of classification, but when searching for the right model we have to try everything to see what will work the best for our prediction. After trying using the Regression model, the results were below average and not satifying, the Logistic Regression model works with binary data where the outcome is true or false. That is not the nature of the project that's why we continued with trying other techniques using classification."]},{"cell_type":"code","execution_count":16,"id":"9c8e713d","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:31.051174Z","iopub.status.busy":"2024-06-27T17:29:31.049852Z","iopub.status.idle":"2024-06-27T17:29:31.55354Z","shell.execute_reply":"2024-06-27T17:29:31.551989Z"},"papermill":{"duration":0.526119,"end_time":"2024-06-27T17:29:31.557948","exception":false,"start_time":"2024-06-27T17:29:31.031829","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0.6376811594202898\n"]}],"source":["from sklearn.linear_model import LogisticRegression\n","lr = LogisticRegression(max_iter=10000)\n","lr.fit(X_train, y_train)\n","y_pred = lr.predict(X_test)\n","print(lr.score(X_test, y_test))"]},{"cell_type":"markdown","id":"0ff690ba","metadata":{"papermill":{"duration":0.032583,"end_time":"2024-06-27T17:29:31.623954","exception":false,"start_time":"2024-06-27T17:29:31.591371","status":"completed"},"tags":[]},"source":["#### **6.2 Decision Tree Classifier**\n","The Decision Tree Classifier implemented in this project has shown promising performance with an accuracy score of 0.866 on the test dataset. This classifier is part of the family of supervised learning algorithms used for classification tasks, where it recursively partitions the data based on features to make predictions. <br><br>\n","The Decision Tree Classifier was initialized using the gini criterion for impurity calculation during node splitting.  The accuracy score of 0.866 indicates that approximately 87% of the predictions made by the model align with the true labels in the test set. With the Decision Tree Classifier, this project demonstrates its utility in addressing classification tasks with a balanced approach to performance and interpretability."]},{"cell_type":"code","execution_count":17,"id":"52abc835","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:31.678198Z","iopub.status.busy":"2024-06-27T17:29:31.677858Z","iopub.status.idle":"2024-06-27T17:29:31.871012Z","shell.execute_reply":"2024-06-27T17:29:31.869603Z"},"papermill":{"duration":0.21652,"end_time":"2024-06-27T17:29:31.873645","exception":false,"start_time":"2024-06-27T17:29:31.657125","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0.8188405797101449\n","              precision    recall  f1-score   support\n","\n","           1       1.00      1.00      1.00         1\n","           2       0.82      0.85      0.83       112\n","           4       0.50      0.53      0.51        17\n","           5       0.86      0.83      0.84       146\n","\n","    accuracy                           0.82       276\n","   macro avg       0.79      0.80      0.80       276\n","weighted avg       0.82      0.82      0.82       276\n","\n"]}],"source":["from sklearn.tree import DecisionTreeClassifier\n","dt = DecisionTreeClassifier(criterion=\"gini\", splitter= \"random\")\n","dt.fit(X_train, y_train)\n","y_pred = dt.predict(X_test)\n","\n","print(dt.score(X_test, y_test))\n","print(classification_report(y_test, y_pred))"]},{"cell_type":"markdown","id":"4bab41ff","metadata":{"papermill":{"duration":0.015121,"end_time":"2024-06-27T17:29:31.90572","exception":false,"start_time":"2024-06-27T17:29:31.890599","status":"completed"},"tags":[]},"source":["Implementing the feature importances from a Decision Tree Classifier we get values between 0 and 1, indicating how much that feature contributed to the model's decision making process. Higher values indicate that the feautre played a more significant role in the decision making process. The features with an importance of 0.000 were likely not used in the splits of the decision tree, indicating they had no impact on the model's predictions. This approach helps to uncover insights into how the model interprets and utilizes different features to make predictions, contributing to the interpretability of the machine learning model."]},{"cell_type":"code","execution_count":18,"id":"bf76671d","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:31.938208Z","iopub.status.busy":"2024-06-27T17:29:31.93784Z","iopub.status.idle":"2024-06-27T17:29:31.946874Z","shell.execute_reply":"2024-06-27T17:29:31.945718Z"},"papermill":{"duration":0.028252,"end_time":"2024-06-27T17:29:31.949384","exception":false,"start_time":"2024-06-27T17:29:31.921132","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Month Received                        0.233063\n","Phone Problem_Charging Port Repair    0.051840\n","Phone Brand_Xiaomi                    0.047056\n","Phone Brand_Samsung                   0.040559\n","Phone Problem_Speaker Replacement     0.030625\n","                                        ...   \n","Phone Model_Mate 20 Lite              0.000000\n","Phone Model_Mate 20 X                 0.000000\n","Phone Model_Mate 30 Lite              0.000000\n","Phone Model_Mate 30 Pro               0.000000\n","Phone Model_Galaxy S9 Plus            0.000000\n","Length: 334, dtype: float64\n"]}],"source":["importances = dt.feature_importances_\n","feature_importances = pd.Series(importances, index=X.columns)\n","print(feature_importances.sort_values(ascending=False))"]},{"cell_type":"markdown","id":"6661cf04","metadata":{"papermill":{"duration":0.014833,"end_time":"2024-06-27T17:29:31.980777","exception":false,"start_time":"2024-06-27T17:29:31.965944","status":"completed"},"tags":[]},"source":["## **7. Classification of Repair Duration into Categories**\n","To facilitate a more intuitive analysis of repair times, we have implemented a function that classifies the repair durations into three categories: \"fast\", \"mid\", and \"slow\". This classification is based on the number of days taken for the repair process:\n","\n","* **Fast**: Repairs completed within 2 days or less.\n","* **Mid**: Repairs completed within 3 to 4 days.\n","* **Slow**: Repairs taking 5 days or more.\n","\n","The function classify_duration takes the repair duration as input and returns the appropriate category. We then apply this function to the 'Repair Duration' column in our dataset, creating a new column 'Duration Category' which holds the category for each repair record. This categorization simplifies the repair duration data, making it easier to visualize and interpret patterns in repair times across different phone brands."]},{"cell_type":"code","execution_count":19,"id":"f4425f0b","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:32.013613Z","iopub.status.busy":"2024-06-27T17:29:32.01323Z","iopub.status.idle":"2024-06-27T17:29:32.021483Z","shell.execute_reply":"2024-06-27T17:29:32.020282Z"},"papermill":{"duration":0.02795,"end_time":"2024-06-27T17:29:32.02426","exception":false,"start_time":"2024-06-27T17:29:31.99631","status":"completed"},"tags":[]},"outputs":[],"source":["def classify_duration(duration):\n","    if duration <= 2:\n","        return 'fast'\n","    elif duration <= 4:\n","        return 'mid'\n","    else:\n","        return 'slow'\n","\n","df['Duration Category'] = df['Repair Duration'].apply(classify_duration)"]},{"cell_type":"code","execution_count":20,"id":"e3fefd11","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:32.069004Z","iopub.status.busy":"2024-06-27T17:29:32.06799Z","iopub.status.idle":"2024-06-27T17:29:32.076807Z","shell.execute_reply":"2024-06-27T17:29:32.075536Z"},"papermill":{"duration":0.037878,"end_time":"2024-06-27T17:29:32.08081","exception":false,"start_time":"2024-06-27T17:29:32.042932","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["0       slow\n","1       slow\n","2        mid\n","3       slow\n","4       slow\n","        ... \n","1118    fast\n","1119    fast\n","1120    fast\n","1121    fast\n","1122    fast\n","Name: Duration Category, Length: 1102, dtype: object\n"]}],"source":["# printing the column 'Duration Category'\n","print(df.iloc[:, -1])"]},{"cell_type":"markdown","id":"03823099","metadata":{"papermill":{"duration":0.01713,"end_time":"2024-06-27T17:29:32.12116","exception":false,"start_time":"2024-06-27T17:29:32.10403","status":"completed"},"tags":[]},"source":["#### **7.1 Visualization of the distribution of durations by category**\n","Most of the instances of repairs are categorized as 'slow', each taking 5 or more days for repair. We have more than 550 instances of 'slow' repair, followed by the category 'fast' which takes 2 days or less. The 'mid' category is the least frequent with less than 100 instances. From this visualization we can conclude that mobile phones are fixed very fast or very slow. That can be because of the month the phone was received for repairing or maybe by the difficulty of the problem, the issue the phone has or maybe the company has problems with ordering mobile parts that have to be installed."]},{"cell_type":"code","execution_count":21,"id":"ffae4a2e","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:32.154263Z","iopub.status.busy":"2024-06-27T17:29:32.153628Z","iopub.status.idle":"2024-06-27T17:29:32.371042Z","shell.execute_reply":"2024-06-27T17:29:32.37007Z"},"papermill":{"duration":0.236282,"end_time":"2024-06-27T17:29:32.373032","exception":false,"start_time":"2024-06-27T17:29:32.13675","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 800x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["category_counts = df['Duration Category'].value_counts()\n","\n","plt.figure(figsize=(8, 6))\n","sns.countplot(x='Duration Category', data=df, order=category_counts.index, palette='viridis')\n","plt.title('Distribution of Repair Duration Categories')\n","plt.xlabel('Duration Category')\n","plt.ylabel('Number of Repaired Phones')\n","plt.show()"]},{"cell_type":"markdown","id":"15142c1f","metadata":{"papermill":{"duration":0.015254,"end_time":"2024-06-27T17:29:32.403811","exception":false,"start_time":"2024-06-27T17:29:32.388557","status":"completed"},"tags":[]},"source":["#### **7.2 Visualization of the Distribution of Durations by Categories by Phone Brand**\n","This visualization demonstrates the distribution of repair duration categories by phone brand. It reveals that Samsung mobile phones exhibit both the fastest and the slowest repair times. Based on my research, Samsung's extensive range of models contributes to this variability. The large number of different models complicates the process for mobile repair shops to keep an adequate supply of parts for each specific model. Additionally, Samsung offers a wide selection of budget-friendly phones, attracting customers who may not afford more expensive brands. This leads to a higher likelihood of these phones needing repairs due to their widespread use and affordability.\n","<br><br>\n","Similarly, the visualization shows that Xiaomi and Huawei have comparable frequencies in repair duration distribution across different categories. iPhones, on the other hand, tend to have slower repair times, ranking as the second slowest brand to repair. They also rank lower in the fast repair category, indicating that iPhones generally take longer to fix compared to other brands displayed in the visualization."]},{"cell_type":"code","execution_count":22,"id":"c60944f4","metadata":{"execution":{"iopub.execute_input":"2024-06-27T17:29:32.437647Z","iopub.status.busy":"2024-06-27T17:29:32.436758Z","iopub.status.idle":"2024-06-27T17:29:32.838881Z","shell.execute_reply":"2024-06-27T17:29:32.837602Z"},"papermill":{"duration":0.421578,"end_time":"2024-06-27T17:29:32.841146","exception":false,"start_time":"2024-06-27T17:29:32.419568","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["<Figure size 1200x800 with 0 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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["brand_category_counts = df.groupby('Duration Category')[df.columns[df.columns.str.startswith('Phone Brand_')]].sum()\n","\n","plt.figure(figsize=(12, 8))\n","brand_category_counts.plot(kind='bar', colormap='viridis')\n","plt.title('Distribution of Repair Duration Categories by Phone Brand')\n","plt.xlabel('Duration Category')\n","plt.ylabel('Count')\n","plt.xticks(rotation=45, ha='right')\n","plt.legend(title='Phone Brand', bbox_to_anchor=(1, 1))\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","id":"574749aa","metadata":{"papermill":{"duration":0.016773,"end_time":"2024-06-27T17:29:32.875994","exception":false,"start_time":"2024-06-27T17:29:32.859221","status":"completed"},"tags":[]},"source":["## **Conclusions**\n","The results demonstrated that machine learning can significantly enhance the efficiency of mobile repair services by providing accurate estimates of repair times. This capability allows repair shops to manage their workflows more effectively and improve customer satisfaction by setting realistic expectations.<br><br>\n","The model that performed the best on our dataset was the Decision Tree Classifier. This model achieved the highest accuracy compared to the others. Decision Trees work by splitting the data into subsets based on the value of input features, creating a tree-like structure of decisions. This approach is particularly effective for our dataset as it can capture non-linear relationships and interactions between features.\n","\n","While Linear Regression provided a straightforward baseline, it struggled with the complex patterns in our data. \n","\n","Overall, this project showcased the potential of data science to solve practical problems in the mobile repair industry. By leveraging historical data and machine learning, we can make informed decisions that streamline repair processes and enhance service delivery. Future work could involve expanding the dataset, incorporating additional features, and refining the models to achieve even higher accuracy and robustness."]},{"cell_type":"markdown","id":"adcc52b8","metadata":{"papermill":{"duration":0.017345,"end_time":"2024-06-27T17:29:32.910522","exception":false,"start_time":"2024-06-27T17:29:32.893177","status":"completed"},"tags":[]},"source":["## **References**\n","\n","1. **Scikit-learn: Neural Networks.** \"Neural Network Models (Supervised)\" [https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification)\n","2. **Scikit-learn: Supervised Learning.** \"Supervised Learning\" [https://scikit-learn.org/stable/supervised_learning.html#supervised-learning](https://scikit-learn.org/stable/supervised_learning.html#supervised-learning)\n","3. **Scikit-learn: MLP Classifier.** \"sklearn.neural_network.MLPClassifier\" [https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html)\n","4. **Scikit-learn: Decision Trees.** \"sklearn.tree.DecisionTreeClassifier\" [https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html](https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html)\n","5. **DataCamp Blog: Classification Machine Learning.** [https://www.datacamp.com/blog/classification-machine-learning?dc_referrer=https%3A%2F%2Fwww.google.com%2F](https://www.datacamp.com/blog/classification-machine-learning?dc_referrer=https%3A%2F%2Fwww.google.com%2F)\n","6. **Kaggle Tutorial: One-Hot Encoding.** \"Using Categorical Data with One-Hot Encoding\" [https://www.kaggle.com/code/dansbecker/using-categorical-data-with-one-hot-encoding](https://www.kaggle.com/code/dansbecker/using-categorical-data-with-one-hot-encoding)\n"]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"datasetId":4709938,"sourceId":7998715,"sourceType":"datasetVersion"}],"dockerImageVersionId":30664,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.13"},"papermill":{"default_parameters":{},"duration":34.786795,"end_time":"2024-06-27T17:29:33.548638","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-06-27T17:28:58.761843","version":"2.5.0"}},"nbformat":4,"nbformat_minor":5}