chapter9 - decision tree regressor model for average dataset

Chapter9/DecisionTree/Regression/README.md

@@ -1 +1,22 @@
-This readme should describe 'what is in the python code'.
+What it does :
+
+    1. Reads a csv file from the google drive, then create a decision tree regressor model on the average dataset. Visualize the tree. Calculate RMSE and MAE.
+
+Dependancies :
+
+    1. sklearn module is needed to be installed in the local machine.
+
+    2. pandas module is needed to be installed in the local machine.
+
+    3. matplotlib module is needed to be installed in the local machine.
+
+    4. gdown module is needed to be installed in the local machine, to download the CSV file from the google drive. 
+
+
+Things to check before running :
+
+    1. Check whether you have given the correct file location of the csv file. 
+
+    2. Check whether you have access to the file. 
+
+    3. Check whether the file format is correct.

Chapter9/DecisionTree/Regression/decision_tree_regression.py

@@ -1,3 +1,117 @@
-# TODO: Create a decision tree model (using scikit-learn) on the averages dataset (regression). 
-#       Visualize the tree. Calculate RMSE and MAE
-# TODO: Code should be well commented.
+'''Copyright (c) 2021 AIClub
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated 
+documentation files (the "Software"), to deal in the Software without restriction, including without 
+limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 
+the Software, and to permit persons to whom the Software is furnished to do so, subject to the following 
+conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial
+portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT 
+LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO 
+EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN 
+AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE 
+OR OTHER DEALINGS IN THE SOFTWARE.'''
+
+
+# Python program to create a decision tree model using scikit-learn on the averages dataset (regression). 
+# Visualize the tree. Calculate RMSE and MAE.
+
+
+# Import pandas module to read the CSV file and to process the tabular data
+import pandas as pd
+
+# Import train_test_split to split data as test and train
+from sklearn.model_selection import train_test_split
+
+# Import Decision Tree Regressor
+from sklearn.tree import DecisionTreeRegressor 
+
+# Import mean_absolute_error and mean_squared_error functions for error calculations
+from sklearn.metrics import mean_absolute_error, mean_squared_error 
+
+# Import sqrt function to get the square root of the mean_squared_error
+from math import sqrt
+
+# Import plot_tree function to visualize the decision tree
+from sklearn.tree import plot_tree
+
+# Import pyplot function to visualize the decision tree
+from matplotlib import pyplot as plt
+
+# Import gdown module to download files from google drive
+import gdown
+
+
+# ------------------------------------ Get the file from the google drive. ------------------------------------------
+
+# Please change the url as needed (make sure you have the access to the file)
+url = 'https://drive.google.com/file/d/1Hxksp6KSjoex0wdER032QypLUYmdLNeg/view?usp=sharing'
+
+# Derive the file id from the url
+file_id = url.split('/')[-2]
+
+# Derive the download url of the file
+download_url = 'https://drive.google.com/uc?id=' + file_id
+
+# Give the location you want to save it in your local machine
+file_location = r'average.csv'
+
+# Download the file from drive to your local machine
+gdown.download(download_url, file_location)
+
+
+# -------------------------------- Create the Decision Tree Regressor model --------------------------------------
+
+# Read the CSV file
+data = pd.read_csv(file_location)
+
+# Print a sample from the csv dataset
+print('\n---------- First 5 rows of the Dataset ----------\n', data.head())
+
+# Please change the target variable according to the dataset
+# You can refer to the dataset details printed in the above step 
+target_column = 'AVERAGE'
+
+# Seperate the training data by removing the target column
+X = data.drop(columns=[target_column])
+
+# Separate the target values
+y = data[target_column].values
+
+# Split the dataset into train and test data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# Create decision tree regressor
+# You can change the max_depth in order to reduce the error
+regressor = DecisionTreeRegressor(max_depth=3)
+
+# Train the model
+regressor.fit(X_train, y_train)
+
+# Predict using test values
+y_pred = regressor.predict(X_test)
+
+
+# --------------------------------------------- Visualize the Tree -------------------------------------------------
+
+# Get the feature names
+feature_names = list(X.columns)
+
+# Plot the decision tree
+fig = plt.figure(figsize=(15,10))
+plot_tree(regressor, feature_names=feature_names, filled=True)
+plt.show()
+
+
+# ---------------------------------------- Calculate the MAE and RMSE -----------------------------------------------
+
+# Calculate the MAE
+MAE = mean_absolute_error(y_test, y_pred)
+print ('\nMean Absolute Error (MAE): ', MAE)
+
+# Calculate the RMSE
+RMSE = sqrt(mean_squared_error(y_test, y_pred))
+print ('\nRoot Mean Squared Error (RMSE): ', RMSE)