├── .gitignore ├── DecisionTree.py ├── Decistion-tree.ipynb ├── README.md ├── RandomForest.py ├── RandomForestShowcase.ipynb ├── RegressionDecisionTree.py ├── ScikitLearnCompare.py ├── data ├── classification │ ├── test.csv │ └── train.csv ├── random_forest │ └── telecom_churn.csv └── regression │ └── auto-mpg.csv └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_* 2 | __pycache__/ -------------------------------------------------------------------------------- /DecisionTree.py: -------------------------------------------------------------------------------- 1 | # Data wrangling 2 | import pandas as pd 3 | 4 | # Array math 5 | import numpy as np 6 | 7 | # Quick value count calculator 8 | from collections import Counter 9 | 10 | 11 | class Node: 12 | """ 13 | Class for creating the nodes for a decision tree 14 | """ 15 | def __init__( 16 | self, 17 | Y: list, 18 | X: pd.DataFrame, 19 | min_samples_split=None, 20 | max_depth=None, 21 | depth=None, 22 | node_type=None, 23 | rule=None 24 | ): 25 | # Saving the data to the node 26 | self.Y = Y 27 | self.X = X 28 | 29 | # Saving the hyper parameters 30 | self.min_samples_split = min_samples_split if min_samples_split else 20 31 | self.max_depth = max_depth if max_depth else 5 32 | 33 | # Default current depth of node 34 | self.depth = depth if depth else 0 35 | 36 | # Extracting all the features 37 | self.features = list(self.X.columns) 38 | 39 | # Type of node 40 | self.node_type = node_type if node_type else 'root' 41 | 42 | # Rule for spliting 43 | self.rule = rule if rule else "" 44 | 45 | # Calculating the counts of Y in the node 46 | self.counts = Counter(Y) 47 | 48 | # Getting the GINI impurity based on the Y distribution 49 | self.gini_impurity = self.get_GINI() 50 | 51 | # Sorting the counts and saving the final prediction of the node 52 | counts_sorted = list(sorted(self.counts.items(), key=lambda item: item[1])) 53 | 54 | # Getting the last item 55 | yhat = None 56 | if len(counts_sorted) > 0: 57 | yhat = counts_sorted[-1][0] 58 | 59 | # Saving to object attribute. This node will predict the class with the most frequent class 60 | self.yhat = yhat 61 | 62 | # Saving the number of observations in the node 63 | self.n = len(Y) 64 | 65 | # Initiating the left and right nodes as empty nodes 66 | self.left = None 67 | self.right = None 68 | 69 | # Default values for splits 70 | self.best_feature = None 71 | self.best_value = None 72 | 73 | @staticmethod 74 | def GINI_impurity(y1_count: int, y2_count: int) -> float: 75 | """ 76 | Given the observations of a binary class calculate the GINI impurity 77 | """ 78 | # Ensuring the correct types 79 | if y1_count is None: 80 | y1_count = 0 81 | 82 | if y2_count is None: 83 | y2_count = 0 84 | 85 | # Getting the total observations 86 | n = y1_count + y2_count 87 | 88 | # If n is 0 then we return the lowest possible gini impurity 89 | if n == 0: 90 | return 0.0 91 | 92 | # Getting the probability to see each of the classes 93 | p1 = y1_count / n 94 | p2 = y2_count / n 95 | 96 | # Calculating GINI 97 | gini = 1 - (p1 ** 2 + p2 ** 2) 98 | 99 | # Returning the gini impurity 100 | return gini 101 | 102 | @staticmethod 103 | def ma(x: np.array, window: int) -> np.array: 104 | """ 105 | Calculates the moving average of the given list. 106 | """ 107 | return np.convolve(x, np.ones(window), 'valid') / window 108 | 109 | def get_GINI(self): 110 | """ 111 | Function to calculate the GINI impurity of a node 112 | """ 113 | # Getting the 0 and 1 counts 114 | y1_count, y2_count = self.counts.get(0, 0), self.counts.get(1, 0) 115 | 116 | # Getting the GINI impurity 117 | return self.GINI_impurity(y1_count, y2_count) 118 | 119 | def best_split(self) -> tuple: 120 | """ 121 | Given the X features and Y targets calculates the best split 122 | for a decision tree 123 | """ 124 | # Creating a dataset for spliting 125 | df = self.X.copy() 126 | df['Y'] = self.Y 127 | 128 | # Getting the GINI impurity for the base input 129 | GINI_base = self.get_GINI() 130 | 131 | # Finding which split yields the best GINI gain 132 | max_gain = 0 133 | 134 | # Default best feature and split 135 | best_feature = None 136 | best_value = None 137 | 138 | for feature in self.features: 139 | # Droping missing values 140 | Xdf = df.dropna().sort_values(feature) 141 | 142 | # Sorting the values and getting the rolling average 143 | xmeans = self.ma(Xdf[feature].unique(), 2) 144 | 145 | for value in xmeans: 146 | # Spliting the dataset 147 | left_counts = Counter(Xdf[Xdf[feature]=value]['Y']) 149 | 150 | # Getting the Y distribution from the dicts 151 | y0_left, y1_left, y0_right, y1_right = left_counts.get(0, 0), left_counts.get(1, 0), right_counts.get(0, 0), right_counts.get(1, 0) 152 | 153 | # Getting the left and right gini impurities 154 | gini_left = self.GINI_impurity(y0_left, y1_left) 155 | gini_right = self.GINI_impurity(y0_right, y1_right) 156 | 157 | # Getting the obs count from the left and the right data splits 158 | n_left = y0_left + y1_left 159 | n_right = y0_right + y1_right 160 | 161 | # Calculating the weights for each of the nodes 162 | w_left = n_left / (n_left + n_right) 163 | w_right = n_right / (n_left + n_right) 164 | 165 | # Calculating the weighted GINI impurity 166 | wGINI = w_left * gini_left + w_right * gini_right 167 | 168 | # Calculating the GINI gain 169 | GINIgain = GINI_base - wGINI 170 | 171 | # Checking if this is the best split so far 172 | if GINIgain > max_gain: 173 | best_feature = feature 174 | best_value = value 175 | 176 | # Setting the best gain to the current one 177 | max_gain = GINIgain 178 | 179 | return (best_feature, best_value) 180 | 181 | def grow_tree(self): 182 | """ 183 | Recursive method to create the decision tree 184 | """ 185 | # Making a df from the data 186 | df = self.X.copy() 187 | df['Y'] = self.Y 188 | 189 | # If there is GINI to be gained, we split further 190 | if (self.depth < self.max_depth) and (self.n >= self.min_samples_split): 191 | 192 | # Getting the best split 193 | best_feature, best_value = self.best_split() 194 | 195 | if best_feature is not None: 196 | # Saving the best split to the current node 197 | self.best_feature = best_feature 198 | self.best_value = best_value 199 | 200 | # Getting the left and right nodes 201 | left_df, right_df = df[df[best_feature]<=best_value].copy(), df[df[best_feature]>best_value].copy() 202 | 203 | # Creating the left and right nodes 204 | left = Node( 205 | left_df['Y'].values.tolist(), 206 | left_df[self.features], 207 | depth=self.depth + 1, 208 | max_depth=self.max_depth, 209 | min_samples_split=self.min_samples_split, 210 | node_type='left_node', 211 | rule=f"{best_feature} <= {round(best_value, 3)}" 212 | ) 213 | 214 | self.left = left 215 | self.left.grow_tree() 216 | 217 | right = Node( 218 | right_df['Y'].values.tolist(), 219 | right_df[self.features], 220 | depth=self.depth + 1, 221 | max_depth=self.max_depth, 222 | min_samples_split=self.min_samples_split, 223 | node_type='right_node', 224 | rule=f"{best_feature} > {round(best_value, 3)}" 225 | ) 226 | 227 | self.right = right 228 | self.right.grow_tree() 229 | 230 | def print_info(self, width=4): 231 | """ 232 | Method to print the infromation about the tree 233 | """ 234 | # Defining the number of spaces 235 | const = int(self.depth * width ** 1.5) 236 | spaces = "-" * const 237 | 238 | if self.node_type == 'root': 239 | print("Root") 240 | else: 241 | print(f"|{spaces} Split rule: {self.rule}") 242 | print(f"{' ' * const} | GINI impurity of the node: {round(self.gini_impurity, 2)}") 243 | print(f"{' ' * const} | Class distribution in the node: {dict(self.counts)}") 244 | print(f"{' ' * const} | Predicted class: {self.yhat}") 245 | 246 | def print_tree(self): 247 | """ 248 | Prints the whole tree from the current node to the bottom 249 | """ 250 | self.print_info() 251 | 252 | if self.left is not None: 253 | self.left.print_tree() 254 | 255 | if self.right is not None: 256 | self.right.print_tree() 257 | 258 | def predict(self, X:pd.DataFrame): 259 | """ 260 | Batch prediction method 261 | """ 262 | predictions = [] 263 | 264 | for _, x in X.iterrows(): 265 | values = {} 266 | for feature in self.features: 267 | values.update({feature: x[feature]}) 268 | 269 | predictions.append(self.predict_obs(values)) 270 | 271 | return predictions 272 | 273 | def predict_obs(self, values: dict) -> int: 274 | """ 275 | Method to predict the class given a set of features 276 | """ 277 | cur_node = self 278 | while cur_node.depth < cur_node.max_depth: 279 | # Traversing the nodes all the way to the bottom 280 | best_feature = cur_node.best_feature 281 | best_value = cur_node.best_value 282 | 283 | if cur_node.n < cur_node.min_samples_split: 284 | break 285 | 286 | if (values.get(best_feature) < best_value): 287 | if self.left is not None: 288 | cur_node = cur_node.left 289 | else: 290 | if self.right is not None: 291 | cur_node = cur_node.right 292 | 293 | return cur_node.yhat 294 | 295 | if __name__ == '__main__': 296 | # Reading data 297 | d = pd.read_csv("data/classification/train.csv")[['Age', 'Fare', 'Survived']].dropna() 298 | 299 | # Constructing the X and Y matrices 300 | X = d[['Age', 'Fare']] 301 | Y = d['Survived'].values.tolist() 302 | 303 | # Initiating the Node 304 | root = Node(Y, X, max_depth=3, min_samples_split=100) 305 | 306 | # Getting teh best split 307 | root.grow_tree() 308 | 309 | # Printing the tree information 310 | root.print_tree() 311 | 312 | # Predicting 313 | Xsubset = X.copy() 314 | Xsubset['yhat'] = root.predict(Xsubset) 315 | print(Xsubset) -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # decision-tree-python 2 | 3 | Decision tree implementation from scratch in python. 4 | 5 | # Virtual environment 6 | 7 | Creating: 8 | 9 | ``` 10 | conda create --name decision-tree python=3.8 11 | ``` 12 | 13 | Activating: 14 | 15 | ``` 16 | conda activate decision-tree 17 | ``` 18 | 19 | Installing packages: 20 | 21 | ``` 22 | pip install -r requirements.txt 23 | ``` 24 | 25 | Registrating the environment in a notebook 26 | 27 | ``` 28 | ipython kernel install --name "decision-tree" --user 29 | ``` 30 | 31 | # Usage 32 | 33 | IMPORTANT: only use numeric features for the **X** matrices. 34 | 35 | Feel free to create a pull request with the additional implementation. 36 | 37 | ## Classification tree 38 | 39 | ``` 40 | # Reading data 41 | d = pd.read_csv("data/classification/train.csv")[['Age', 'Fare', 'Survived']].dropna() 42 | 43 | # Constructing the X and Y matrices 44 | X = d[['Age', 'Fare']] 45 | Y = d['Survived'].values.tolist() 46 | 47 | # Initiating the Node 48 | root = Node(Y, X, max_depth=3, min_samples_split=100) 49 | 50 | # Getting teh best split 51 | root.grow_tree() 52 | 53 | # Printing the tree information 54 | root.print_tree() 55 | ``` 56 | 57 | ## Regression tree 58 | 59 | ``` 60 | # Reading data 61 | d = pd.read_csv("data/regression/auto-mpg.csv") 62 | 63 | # Subsetting 64 | d = d[d['horsepower']!='?'] 65 | 66 | # Constructing the X and Y matrices 67 | features = ['horsepower', 'weight', 'acceleration'] 68 | 69 | for ft in features: 70 | d[ft] = pd.to_numeric(d[ft]) 71 | 72 | X = d[features] 73 | Y = d['mpg'].values.tolist() 74 | 75 | # Initiating the Node 76 | root = NodeRegression(Y, X, max_depth=3, min_samples_split=3) 77 | 78 | # Growing the tree 79 | root.grow_tree() 80 | 81 | # Printing tree 82 | root.print_tree() 83 | ``` -------------------------------------------------------------------------------- /RandomForest.py: -------------------------------------------------------------------------------- 1 | """ 2 | Code that houses the class that creates and uses the random forest classifier 3 | """ 4 | # Data wrangling 5 | import pandas as pd 6 | 7 | # Numerical operations 8 | import numpy as np 9 | 10 | # Random selections 11 | import random 12 | 13 | # Quick value count calculator 14 | from collections import Counter 15 | 16 | # Tree growth tracking 17 | from tqdm import tqdm 18 | 19 | # Accuracy metrics 20 | from sklearn.metrics import precision_score, recall_score 21 | 22 | 23 | class RandomForestTree(): 24 | """ 25 | Class that grows one random forest tree 26 | """ 27 | def __init__( 28 | self, 29 | Y, 30 | X, 31 | min_samples_split=None, 32 | max_depth=None, 33 | depth=None, 34 | X_features_fraction=None, 35 | node_type=None, 36 | rule=None 37 | ): 38 | # Saving the data for the random forest 39 | self.Y = Y 40 | self.X = X 41 | 42 | # Saving the hyper parameters 43 | self.min_samples_split = min_samples_split if min_samples_split else 20 44 | self.max_depth = max_depth if max_depth else 5 45 | 46 | # Default current depth of the tree 47 | self.depth = depth if depth else 0 48 | 49 | # Saving the final feature list 50 | self.features = list(X.columns) 51 | 52 | # Type of node 53 | self.node_type = node_type if node_type else 'root' 54 | 55 | # Rule for spliting 56 | self.rule = rule if rule else "" 57 | 58 | # Calculating the counts of Y in the node 59 | self.counts = Counter(Y) 60 | 61 | # Getting the GINI impurity based on the Y distribution 62 | self.gini_impurity = self.get_GINI() 63 | 64 | # Getting the number of features 65 | self.n_features = len(self.features) 66 | 67 | # Saving the hyper parameters specific to the random forest 68 | self.X_features_fraction = X_features_fraction if X_features_fraction is not None else 1.0 69 | 70 | # Sorting the counts and saving the final prediction of the node 71 | counts_sorted = list(sorted(self.counts.items(), key=lambda item: item[1])) 72 | 73 | # Getting the last item 74 | yhat = None 75 | if len(counts_sorted) > 0: 76 | yhat = counts_sorted[-1][0] 77 | 78 | # Saving to object attribute. This node will predict the class with the most frequent class 79 | self.yhat = yhat 80 | 81 | # Saving the number of observations in the node 82 | self.n = len(Y) 83 | 84 | # Initiating the left and right nodes as empty nodes 85 | self.left = None 86 | self.right = None 87 | 88 | # Default values for splits 89 | self.best_feature = None 90 | self.best_value = None 91 | 92 | def get_random_X_colsample(self): 93 | # Getting the random subset of features 94 | n_ft = int(self.n_features * self.X_features_fraction) 95 | 96 | # Selecting random features without repetition 97 | features = random.sample(self.features, n_ft) 98 | 99 | # Subseting the X to chosen features 100 | X = self.X[features].copy() 101 | 102 | # Returning the subseted features 103 | return X 104 | 105 | @staticmethod 106 | def GINI_impurity(y1_count: int, y2_count: int) -> float: 107 | """ 108 | Given the observations of a binary class calculate the GINI impurity 109 | """ 110 | # Ensuring the correct types 111 | if y1_count is None: 112 | y1_count = 0 113 | 114 | if y2_count is None: 115 | y2_count = 0 116 | 117 | # Getting the total observations 118 | n = y1_count + y2_count 119 | 120 | # If n is 0 then we return the lowest possible gini impurity 121 | if n == 0: 122 | return 0.0 123 | 124 | # Getting the probability to see each of the classes 125 | p1 = y1_count / n 126 | p2 = y2_count / n 127 | 128 | # Calculating GINI 129 | gini = 1 - (p1 ** 2 + p2 ** 2) 130 | 131 | # Returning the gini impurity 132 | return gini 133 | 134 | @staticmethod 135 | def ma(x: np.array, window: int) -> np.array: 136 | """ 137 | Calculates the moving average of the given list. 138 | """ 139 | return np.convolve(x, np.ones(window), 'valid') / window 140 | 141 | def get_GINI(self): 142 | """ 143 | Function to calculate the GINI impurity of a node 144 | """ 145 | # Getting the 0 and 1 counts 146 | y1_count, y2_count = self.counts.get(0, 0), self.counts.get(1, 0) 147 | 148 | # Getting the GINI impurity 149 | return self.GINI_impurity(y1_count, y2_count) 150 | 151 | def best_split(self) -> tuple: 152 | """ 153 | Given the X features and Y targets calculates the best split 154 | for a decision tree 155 | """ 156 | # Creating a dataset for spliting 157 | df = self.X.copy() 158 | df['Y'] = self.Y 159 | 160 | # Getting the GINI impurity for the base input 161 | GINI_base = self.get_GINI() 162 | 163 | # Finding which split yields the best GINI gain 164 | max_gain = 0 165 | 166 | # Default best feature and split 167 | best_feature = None 168 | best_value = None 169 | 170 | # Getting a random subsample of features 171 | n_ft = int(self.n_features * self.X_features_fraction) 172 | 173 | # Selecting random features without repetition 174 | features_subsample = random.sample(self.features, n_ft) 175 | 176 | for feature in features_subsample: 177 | # Droping missing values 178 | Xdf = df.dropna().sort_values(feature) 179 | 180 | # Sorting the values and getting the rolling average 181 | xmeans = self.ma(Xdf[feature].unique(), 2) 182 | 183 | for value in xmeans: 184 | # Spliting the dataset 185 | left_counts = Counter(Xdf[Xdf[feature]=value]['Y']) 187 | 188 | # Getting the Y distribution from the dicts 189 | y0_left, y1_left, y0_right, y1_right = left_counts.get(0, 0), left_counts.get(1, 0), right_counts.get(0, 0), right_counts.get(1, 0) 190 | 191 | # Getting the left and right gini impurities 192 | gini_left = self.GINI_impurity(y0_left, y1_left) 193 | gini_right = self.GINI_impurity(y0_right, y1_right) 194 | 195 | # Getting the obs count from the left and the right data splits 196 | n_left = y0_left + y1_left 197 | n_right = y0_right + y1_right 198 | 199 | # Calculating the weights for each of the nodes 200 | w_left = n_left / (n_left + n_right) 201 | w_right = n_right / (n_left + n_right) 202 | 203 | # Calculating the weighted GINI impurity 204 | wGINI = w_left * gini_left + w_right * gini_right 205 | 206 | # Calculating the GINI gain 207 | GINIgain = GINI_base - wGINI 208 | 209 | # Checking if this is the best split so far 210 | if GINIgain > max_gain: 211 | best_feature = feature 212 | best_value = value 213 | 214 | # Setting the best gain to the current one 215 | max_gain = GINIgain 216 | 217 | return (best_feature, best_value) 218 | 219 | def grow_tree(self): 220 | """ 221 | Recursive method to create the decision tree 222 | """ 223 | # If there is GINI to be gained, we split further 224 | if (self.depth < self.max_depth) and (self.n >= self.min_samples_split): 225 | 226 | # Getting the best split 227 | best_feature, best_value = self.best_split() 228 | 229 | if best_feature is not None: 230 | # Saving the best split to the current node 231 | self.best_feature = best_feature 232 | self.best_value = best_value 233 | 234 | # Getting the left and right dataframe indexes 235 | left_index, right_index = self.X[self.X[best_feature]<=best_value].index, self.X[self.X[best_feature]>best_value].index 236 | 237 | # Extracting the left X and right X 238 | left_X, right_X = self.X[self.X.index.isin(left_index)], self.X[self.X.index.isin(right_index)] 239 | 240 | # Reseting the indexes 241 | left_X.reset_index(inplace=True, drop=True) 242 | right_X.reset_index(inplace=True, drop=True) 243 | 244 | # Extracting the left Y and the right Y 245 | left_Y, right_Y = [self.Y[x] for x in left_index], [self.Y[x] for x in right_index] 246 | 247 | # Creating the left and right nodes 248 | left = RandomForestTree( 249 | left_Y, 250 | left_X, 251 | depth=self.depth + 1, 252 | max_depth=self.max_depth, 253 | min_samples_split=self.min_samples_split, 254 | node_type='left_node', 255 | rule=f"{best_feature} <= {round(best_value, 3)}" 256 | ) 257 | 258 | self.left = left 259 | self.left.grow_tree() 260 | 261 | right = RandomForestTree( 262 | right_Y, 263 | right_X, 264 | depth=self.depth + 1, 265 | max_depth=self.max_depth, 266 | min_samples_split=self.min_samples_split, 267 | node_type='right_node', 268 | rule=f"{best_feature} > {round(best_value, 3)}" 269 | ) 270 | 271 | self.right = right 272 | self.right.grow_tree() 273 | 274 | def predict(self, X:pd.DataFrame): 275 | """ 276 | Batch prediction method 277 | """ 278 | predictions = [] 279 | 280 | for _, x in X.iterrows(): 281 | values = {} 282 | for feature in self.features: 283 | values.update({feature: x[feature]}) 284 | 285 | predictions.append(self.predict_obs(values)) 286 | 287 | return predictions 288 | 289 | def predict_obs(self, values: dict) -> int: 290 | """ 291 | Method to predict the class given a set of features 292 | """ 293 | cur_node = self 294 | while cur_node.depth < cur_node.max_depth: 295 | # Traversing the nodes all the way to the bottom 296 | best_feature = cur_node.best_feature 297 | best_value = cur_node.best_value 298 | 299 | if (cur_node.n < cur_node.min_samples_split) | (best_feature is None): 300 | break 301 | 302 | if (values.get(best_feature) < best_value): 303 | if self.left is not None: 304 | cur_node = cur_node.left 305 | else: 306 | if self.right is not None: 307 | cur_node = cur_node.right 308 | 309 | return cur_node.yhat 310 | 311 | def print_info(self, width=4): 312 | """ 313 | Method to print the infromation about the tree 314 | """ 315 | # Defining the number of spaces 316 | const = int(self.depth * width ** 1.5) 317 | spaces = "-" * const 318 | 319 | if self.node_type == 'root': 320 | print("Root") 321 | else: 322 | print(f"|{spaces} Split rule: {self.rule}") 323 | print(f"{' ' * const} | GINI impurity of the node: {round(self.gini_impurity, 2)}") 324 | print(f"{' ' * const} | Class distribution in the node: {dict(self.counts)}") 325 | print(f"{' ' * const} | Predicted class: {self.yhat}") 326 | 327 | def print_tree(self): 328 | """ 329 | Prints the whole tree from the current node to the bottom 330 | """ 331 | self.print_info() 332 | 333 | if self.left is not None: 334 | self.left.print_tree() 335 | 336 | if self.right is not None: 337 | self.right.print_tree() 338 | 339 | 340 | class RandomForestClassifier(): 341 | """ 342 | Class that creates a random forest for classification problems 343 | """ 344 | def __init__( 345 | self, 346 | Y: list, 347 | X: pd.DataFrame, 348 | min_samples_split=None, 349 | max_depth=None, 350 | n_trees=None, 351 | X_features_fraction=None, 352 | X_obs_fraction=None 353 | ): 354 | # Saving the data for the random forest 355 | self.Y = Y 356 | self.X = X 357 | 358 | # Saving the hyper parameters 359 | self.min_samples_split = min_samples_split if min_samples_split else 20 360 | self.max_depth = max_depth if max_depth else 5 361 | 362 | # Saving the final feature list 363 | self.features = list(X.columns) 364 | 365 | # Getting the number of features 366 | self.n_features = len(self.features) 367 | 368 | # Saving the hyper parameters specific to the random forest 369 | self.n_trees = n_trees if n_trees is not None else 30 370 | self.X_features_fraction = X_features_fraction if X_features_fraction is not None else 1.0 371 | self.X_obs_fraction = X_obs_fraction if X_obs_fraction is not None else 1.0 372 | 373 | def bootstrap_sample(self): 374 | """ 375 | Function that creates a bootstraped sample with the class instance parameters 376 | """ 377 | # Sampling the number of rows with repetition 378 | Xbootstrap = self.X.sample(frac=self.X_obs_fraction, replace=True) 379 | 380 | # Getting the index of samples 381 | indexes = Xbootstrap.index 382 | 383 | # Getting the corresponding Y variables 384 | Ybootstrap = [self.Y[x] for x in indexes] 385 | 386 | # Droping the index of X 387 | Xbootstrap.reset_index(inplace=True, drop=True) 388 | 389 | # Returning the X, Y pair 390 | return Xbootstrap, Ybootstrap 391 | 392 | def grow_random_forest(self): 393 | """ 394 | Main method of the class; Creates **n_trees** random trees 395 | """ 396 | # List to hold trees in 397 | random_forest = [] 398 | 399 | # Iterating 400 | for _ in tqdm(range(self.n_trees)): 401 | # Getting the bootstrapped sample 402 | X, Y = self.bootstrap_sample() 403 | 404 | # Initiating the random tree 405 | tree = RandomForestTree( 406 | Y=Y, 407 | X=X, 408 | min_samples_split=self.min_samples_split, 409 | max_depth=self.max_depth, 410 | X_features_fraction=self.X_features_fraction 411 | ) 412 | 413 | # Growing the tree 414 | tree.grow_tree() 415 | 416 | # Appending the tree to the list of trees (the forest) 417 | random_forest.append(tree) 418 | 419 | # Saving the random forest list to memory 420 | self.random_forest = random_forest 421 | 422 | def print_trees(self): 423 | """ 424 | Method to print out all the grown trees in the classifier 425 | """ 426 | for i in range(self.n_trees): 427 | print("------ \n") 428 | print(f"Tree number: {i + 1} \n") 429 | self.random_forest[i].print_tree() 430 | print("------ \n") 431 | 432 | def tree_predictions(self, X: pd.DataFrame) -> list: 433 | """ 434 | Method to get the predictions from all the trees 435 | """ 436 | predictions = [] 437 | for i in range(self.n_trees): 438 | yhat = self.random_forest[i].predict(X) 439 | 440 | # Apending to prediction placeholder 441 | predictions.append(yhat) 442 | 443 | # Returning the prediction list 444 | return predictions 445 | 446 | def predict(self, X: pd.DataFrame) -> list: 447 | """ 448 | Method to get the final prediction of the whole random forest 449 | """ 450 | # Getting the individual tree predictions 451 | yhat = self.tree_predictions(X) 452 | 453 | # Saving the number of obs in X 454 | n = X.shape[0] 455 | 456 | # Getting the majority vote of each coordinate of the prediction list 457 | yhat_final = [] 458 | 459 | for i in range(n): 460 | yhat_obs = [x[i] for x in yhat] 461 | 462 | # Getting the most frequent entry 463 | counts = Counter(yhat_obs) 464 | most_common = counts.most_common(1)[0][0] 465 | 466 | # Appending the most common entry to final yhat list 467 | yhat_final.append(most_common) 468 | 469 | # Returning the final predictions 470 | return yhat_final 471 | 472 | if __name__ == '__main__': 473 | # Reading data for classification 474 | d = pd.read_csv("data/random_forest/telecom_churn.csv") 475 | 476 | # Setting the features used 477 | features = [ 478 | 'AccountWeeks', 479 | 'DataUsage', 480 | 'DayMins', 481 | 'DayCalls', 482 | 'MonthlyCharge', 483 | 'OverageFee', 484 | 'RoamMins' 485 | ] 486 | 487 | # Initiating the random forest object 488 | rf = RandomForestClassifier( 489 | Y=d['Churn'], 490 | X=d[features], 491 | min_samples_split=5, 492 | max_depth=4, 493 | n_trees=10, 494 | X_features_fraction=0.5 495 | ) 496 | 497 | # Growing the random forest 498 | rf.grow_random_forest() 499 | 500 | # Printing out the trees 501 | rf.print_trees() 502 | 503 | # Making predictions 504 | yhat = rf.predict(d[features]) 505 | d['yhat'] = yhat 506 | 507 | # Measurring accuracy 508 | print(f"The training precision: {precision_score(d['Churn'], d['yhat'])}") 509 | print(f"The training recall: {recall_score(d['Churn'], d['yhat'])}") -------------------------------------------------------------------------------- /RandomForestShowcase.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Importing packages " 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "# Data wrangling \n", 17 | "import pandas as pd \n", 18 | "\n", 19 | "# Array math\n", 20 | "import numpy as np \n", 21 | "\n", 22 | "# Ploting \n", 23 | "import seaborn as sns\n", 24 | "import matplotlib.pyplot as plt\n", 25 | "\n", 26 | "# List iteration tracking\n", 27 | "from tqdm import tqdm\n", 28 | "\n", 29 | "# Importing the custom written class \n", 30 | "from DecisionTree import Node \n", 31 | "\n", 32 | "# Importing the custom regression tree \n", 33 | "from RandomForest import RandomForestClassifier, RandomForestTree\n", 34 | "\n", 35 | "# Time tracking\n", 36 | "import time\n", 37 | "\n", 38 | "# Precision metrics \n", 39 | "from sklearn.metrics import precision_score, recall_score" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "# Reading data " 47 | ] 48 | }, 49 | { 50 | "cell_type": "markdown", 51 | "metadata": {}, 52 | "source": [ 53 | "The data regards telecom churn. \n", 54 | "\n", 55 | "The objective is to create a model that predicts whether a customer will quit using the features available." 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 2, 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "d = pd.read_csv('data/random_forest/telecom_churn.csv')" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 3, 70 | "metadata": {}, 71 | "outputs": [ 72 | { 73 | "name": "stdout", 74 | "output_type": "stream", 75 | "text": [ 76 | "Data shape: (3333, 11)\n" 77 | ] 78 | } 79 | ], 80 | "source": [ 81 | "print(f\"Data shape: {d.shape}\")" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 4, 87 | "metadata": {}, 88 | "outputs": [ 89 | { 90 | "data": { 91 | "text/html": [ 92 | "
\n", 93 | "\n", 106 | "\n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | "
ChurnAccountWeeksContractRenewalDataPlanDataUsageCustServCallsDayMinsDayCallsMonthlyChargeOverageFeeRoamMins
00128112.71265.111089.09.8710.0
10107113.71161.612382.09.7813.7
20137100.00243.411452.06.0612.2
3084000.02299.47157.03.106.6
4075000.03166.711341.07.4210.1
\n", 196 | "
" 197 | ], 198 | "text/plain": [ 199 | " Churn AccountWeeks ContractRenewal DataPlan DataUsage CustServCalls \\\n", 200 | "0 0 128 1 1 2.7 1 \n", 201 | "1 0 107 1 1 3.7 1 \n", 202 | "2 0 137 1 0 0.0 0 \n", 203 | "3 0 84 0 0 0.0 2 \n", 204 | "4 0 75 0 0 0.0 3 \n", 205 | "\n", 206 | " DayMins DayCalls MonthlyCharge OverageFee RoamMins \n", 207 | "0 265.1 110 89.0 9.87 10.0 \n", 208 | "1 161.6 123 82.0 9.78 13.7 \n", 209 | "2 243.4 114 52.0 6.06 12.2 \n", 210 | "3 299.4 71 57.0 3.10 6.6 \n", 211 | "4 166.7 113 41.0 7.42 10.1 " 212 | ] 213 | }, 214 | "execution_count": 4, 215 | "metadata": {}, 216 | "output_type": "execute_result" 217 | } 218 | ], 219 | "source": [ 220 | "d.head()" 221 | ] 222 | }, 223 | { 224 | "cell_type": "code", 225 | "execution_count": 5, 226 | "metadata": {}, 227 | "outputs": [ 228 | { 229 | "data": { 230 | "text/plain": [ 231 | "Churn int64\n", 232 | "AccountWeeks int64\n", 233 | "ContractRenewal int64\n", 234 | "DataPlan int64\n", 235 | "DataUsage float64\n", 236 | "CustServCalls int64\n", 237 | "DayMins float64\n", 238 | "DayCalls int64\n", 239 | "MonthlyCharge float64\n", 240 | "OverageFee float64\n", 241 | "RoamMins float64\n", 242 | "dtype: object" 243 | ] 244 | }, 245 | "execution_count": 5, 246 | "metadata": {}, 247 | "output_type": "execute_result" 248 | } 249 | ], 250 | "source": [ 251 | "d.dtypes" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 6, 257 | "metadata": {}, 258 | "outputs": [ 259 | { 260 | "data": { 261 | "text/plain": [ 262 | "Churn\n", 263 | "0 2850\n", 264 | "1 483\n", 265 | "dtype: int64" 266 | ] 267 | }, 268 | "execution_count": 6, 269 | "metadata": {}, 270 | "output_type": "execute_result" 271 | } 272 | ], 273 | "source": [ 274 | "# Distribution of churn in data \n", 275 | "d.groupby('Churn').size()" 276 | ] 277 | }, 278 | { 279 | "cell_type": "markdown", 280 | "metadata": {}, 281 | "source": [ 282 | "# Random forest - quick theory review" 283 | ] 284 | }, 285 | { 286 | "cell_type": "markdown", 287 | "metadata": {}, 288 | "source": [ 289 | "The classifier which will be created is a random forest classifier. \n", 290 | "\n", 291 | "Lets denote it as **rf()**. " 292 | ] 293 | }, 294 | { 295 | "cell_type": "markdown", 296 | "metadata": {}, 297 | "source": [ 298 | "Given a set of input matrix $\\mathbb{X}_{nxp}$ the classifier **rf()** outputs either 1 or 0.\n", 299 | "\n", 300 | "$$rf: \\mathbb{X} \\rightarrow \\{1, 0\\}$$" 301 | ] 302 | }, 303 | { 304 | "cell_type": "markdown", 305 | "metadata": {}, 306 | "source": [ 307 | "The algorithm of the random forest grows **k** decision trees. \n", 308 | "\n", 309 | "The final prediction of the **rf()** classifier is a majority vote: the input matrix $\\mathbb{X}$ is used with each of the **k** trees, and then the class with the most outputs wins. \n", 310 | "\n", 311 | "In the notebook about decision trees it is clear that with the same input and the same hyperparameters, the same output and the same rules will be learnt by a decision tree. So why grow **k** of them? " 312 | ] 313 | }, 314 | { 315 | "cell_type": "markdown", 316 | "metadata": {}, 317 | "source": [ 318 | "## Data bootstrapping" 319 | ] 320 | }, 321 | { 322 | "cell_type": "markdown", 323 | "metadata": {}, 324 | "source": [ 325 | "The random in the random forest starts at the data sample creation for each of the decision trees. The technique used in creating **k** datasamples is bootstrapping\n", 326 | "\n", 327 | "Given a dataset of n rows and p features: we sample the rows from the original dataset with replacement. For every new decision tree *i*, a new bootsrapped dataset is created: $\\mathbb{X_{b}^{i}}$.\n", 328 | "\n", 329 | "For example, lets assume that the whole dataset has 10 rows of data:" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": 7, 335 | "metadata": {}, 336 | "outputs": [ 337 | { 338 | "name": "stdout", 339 | "output_type": "stream", 340 | "text": [ 341 | " Churn DataPlan DayMins OverageFee\n", 342 | "0 0 0 156.2 4.50\n", 343 | "1 0 0 259.3 8.76\n", 344 | "2 0 0 247.4 8.80\n", 345 | "3 0 0 149.7 10.63\n", 346 | "4 0 1 155.9 8.12\n", 347 | "5 0 0 191.0 15.94\n", 348 | "6 0 0 146.0 5.49\n", 349 | "7 0 0 203.7 10.82\n", 350 | "8 0 0 162.7 14.60\n", 351 | "9 0 0 219.4 11.29\n" 352 | ] 353 | } 354 | ], 355 | "source": [ 356 | "# Lets imagine this the whole dataset\n", 357 | "dsubset = d.sample(10).copy()[['Churn', 'DataPlan', 'DayMins', 'OverageFee']]\n", 358 | "dsubset.reset_index(inplace=True, drop=True)\n", 359 | "\n", 360 | "print(dsubset)" 361 | ] 362 | }, 363 | { 364 | "cell_type": "markdown", 365 | "metadata": {}, 366 | "source": [ 367 | "To create 3 more random bootsrapped samples we use the pandas function **sample(replace=True)**. The key concept is that the sampling is done *with replacement*: the same rows might appear several times in our sample. " 368 | ] 369 | }, 370 | { 371 | "cell_type": "code", 372 | "execution_count": 8, 373 | "metadata": {}, 374 | "outputs": [ 375 | { 376 | "name": "stdout", 377 | "output_type": "stream", 378 | "text": [ 379 | "----- \n", 380 | "\n", 381 | "Boostrapped sample: 1 \n", 382 | "\n", 383 | " Churn DataPlan DayMins OverageFee\n", 384 | "4 0 1 155.9 8.12\n", 385 | "1 0 0 259.3 8.76\n", 386 | "1 0 0 259.3 8.76\n", 387 | "4 0 1 155.9 8.12\n", 388 | "4 0 1 155.9 8.12\n", 389 | "5 0 0 191.0 15.94\n", 390 | "5 0 0 191.0 15.94\n", 391 | "5 0 0 191.0 15.94\n", 392 | "0 0 0 156.2 4.50\n", 393 | "4 0 1 155.9 8.12\n", 394 | "----- \n", 395 | "\n", 396 | "----- \n", 397 | "\n", 398 | "Boostrapped sample: 2 \n", 399 | "\n", 400 | " Churn DataPlan DayMins OverageFee\n", 401 | "7 0 0 203.7 10.82\n", 402 | "8 0 0 162.7 14.60\n", 403 | "4 0 1 155.9 8.12\n", 404 | "2 0 0 247.4 8.80\n", 405 | "1 0 0 259.3 8.76\n", 406 | "4 0 1 155.9 8.12\n", 407 | "0 0 0 156.2 4.50\n", 408 | "1 0 0 259.3 8.76\n", 409 | "9 0 0 219.4 11.29\n", 410 | "4 0 1 155.9 8.12\n", 411 | "----- \n", 412 | "\n", 413 | "----- \n", 414 | "\n", 415 | "Boostrapped sample: 3 \n", 416 | "\n", 417 | " Churn DataPlan DayMins OverageFee\n", 418 | "8 0 0 162.7 14.60\n", 419 | "3 0 0 149.7 10.63\n", 420 | "9 0 0 219.4 11.29\n", 421 | "7 0 0 203.7 10.82\n", 422 | "4 0 1 155.9 8.12\n", 423 | "3 0 0 149.7 10.63\n", 424 | "1 0 0 259.3 8.76\n", 425 | "3 0 0 149.7 10.63\n", 426 | "1 0 0 259.3 8.76\n", 427 | "4 0 1 155.9 8.12\n", 428 | "----- \n", 429 | "\n" 430 | ] 431 | } 432 | ], 433 | "source": [ 434 | "for i, _ in enumerate(range(3)):\n", 435 | " print(\"----- \\n\")\n", 436 | " print(f\"Boostrapped sample: {i + 1} \\n\")\n", 437 | " print(dsubset.sample(frac=1.0, replace=True))\n", 438 | " print(\"----- \\n\")" 439 | ] 440 | }, 441 | { 442 | "cell_type": "markdown", 443 | "metadata": {}, 444 | "source": [ 445 | "For each of the **k** trees grown in random forest, we create **k** bootstrapped data samples. " 446 | ] 447 | }, 448 | { 449 | "cell_type": "markdown", 450 | "metadata": {}, 451 | "source": [ 452 | "## Feature selection at each split " 453 | ] 454 | }, 455 | { 456 | "cell_type": "markdown", 457 | "metadata": {}, 458 | "source": [ 459 | "Now that we have a dataset $\\mathbb{X_{b}^{i}}$ for each of the **k** trees the final part is to determine the splitting criterion for the creation of the nodes. \n", 460 | "\n", 461 | "In the classification case, the gini gain criterion is the same as in the simple decision tree case. The difference is that at each node splitting, a random subsample of collumns are select to find the \"best split\". \n", 462 | "\n", 463 | "For example, if we have 10 collumns as features and we select the hyperparameter of **X_features_fraction = 0.8** then at each node where the best split is beeing calculated, we would select 8 random features (10 * 0.8 = 8). " 464 | ] 465 | }, 466 | { 467 | "cell_type": "markdown", 468 | "metadata": {}, 469 | "source": [ 470 | "# Features to use " 471 | ] 472 | }, 473 | { 474 | "cell_type": "markdown", 475 | "metadata": {}, 476 | "source": [ 477 | "The bellow feature list will be used in the creation of the random forest. " 478 | ] 479 | }, 480 | { 481 | "cell_type": "code", 482 | "execution_count": 9, 483 | "metadata": {}, 484 | "outputs": [], 485 | "source": [ 486 | "# Defining the feature list used in the growth of the tree\n", 487 | "features = [\n", 488 | " 'AccountWeeks',\n", 489 | " 'DataUsage',\n", 490 | " 'DayMins',\n", 491 | " 'DayCalls',\n", 492 | " 'MonthlyCharge',\n", 493 | " 'OverageFee',\n", 494 | " 'RoamMins'\n", 495 | "]" 496 | ] 497 | }, 498 | { 499 | "cell_type": "code", 500 | "execution_count": 10, 501 | "metadata": {}, 502 | "outputs": [ 503 | { 504 | "data": { 505 | "text/html": [ 506 | "
\n", 507 | "\n", 520 | "\n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | "
AccountWeeksDataUsageDayMinsDayCallsMonthlyChargeOverageFeeRoamMinsChurn
977933.19134.210568.98.1311.81
3104632.78214.26179.89.0610.30
355310.28166.110537.83.9712.70
8451442.70283.99892.09.6010.00
19431250.00168.69944.08.7810.90
1219360.00178.68349.010.6610.90
94900.00179.17147.09.5310.60
2031050.00140.610940.08.936.80
14231272.8195.911758.17.9810.40
1370960.00179.512545.08.126.60
\n", 647 | "
" 648 | ], 649 | "text/plain": [ 650 | " AccountWeeks DataUsage DayMins DayCalls MonthlyCharge OverageFee \\\n", 651 | "977 93 3.19 134.2 105 68.9 8.13 \n", 652 | "3104 63 2.78 214.2 61 79.8 9.06 \n", 653 | "355 31 0.28 166.1 105 37.8 3.97 \n", 654 | "845 144 2.70 283.9 98 92.0 9.60 \n", 655 | "1943 125 0.00 168.6 99 44.0 8.78 \n", 656 | "1219 36 0.00 178.6 83 49.0 10.66 \n", 657 | "94 90 0.00 179.1 71 47.0 9.53 \n", 658 | "203 105 0.00 140.6 109 40.0 8.93 \n", 659 | "1423 127 2.81 95.9 117 58.1 7.98 \n", 660 | "1370 96 0.00 179.5 125 45.0 8.12 \n", 661 | "\n", 662 | " RoamMins Churn \n", 663 | "977 11.8 1 \n", 664 | "3104 10.3 0 \n", 665 | "355 12.7 0 \n", 666 | "845 10.0 0 \n", 667 | "1943 10.9 0 \n", 668 | "1219 10.9 0 \n", 669 | "94 10.6 0 \n", 670 | "203 6.8 0 \n", 671 | "1423 10.4 0 \n", 672 | "1370 6.6 0 " 673 | ] 674 | }, 675 | "execution_count": 10, 676 | "metadata": {}, 677 | "output_type": "execute_result" 678 | } 679 | ], 680 | "source": [ 681 | "d[features + ['Churn']].sample(10)" 682 | ] 683 | }, 684 | { 685 | "cell_type": "markdown", 686 | "metadata": {}, 687 | "source": [ 688 | "# Creating the train and test sets " 689 | ] 690 | }, 691 | { 692 | "cell_type": "code", 693 | "execution_count": 11, 694 | "metadata": {}, 695 | "outputs": [ 696 | { 697 | "name": "stdout", 698 | "output_type": "stream", 699 | "text": [ 700 | "Total rows in the dataset: 3333\n", 701 | "Rows in training set: 2500\n", 702 | "Rows in test set: 833\n" 703 | ] 704 | } 705 | ], 706 | "source": [ 707 | "# Fraction of rows in the training set \n", 708 | "train_share = 0.75\n", 709 | "\n", 710 | "# Creating the train and test sets\n", 711 | "train = d.sample(frac=train_share)\n", 712 | "test = d[~d.index.isin(train.index)].copy()\n", 713 | "\n", 714 | "print(f\"Total rows in the dataset: {d.shape[0]}\")\n", 715 | "print(f\"Rows in training set: {train.shape[0]}\")\n", 716 | "print(f\"Rows in test set: {test.shape[0]}\")" 717 | ] 718 | }, 719 | { 720 | "cell_type": "markdown", 721 | "metadata": {}, 722 | "source": [ 723 | "# Training the random forest " 724 | ] 725 | }, 726 | { 727 | "cell_type": "code", 728 | "execution_count": 12, 729 | "metadata": {}, 730 | "outputs": [ 731 | { 732 | "name": "stderr", 733 | "output_type": "stream", 734 | "text": [ 735 | "100%|██████████████████████████████████████████████████████████████████████████████████| 30/30 [02:34<00:00, 5.14s/it]\n" 736 | ] 737 | } 738 | ], 739 | "source": [ 740 | "# Initiating the random forest object \n", 741 | "rf = RandomForestClassifier(\n", 742 | " Y=train['Churn'], \n", 743 | " X=train[features],\n", 744 | " min_samples_split=5,\n", 745 | " max_depth=3,\n", 746 | " n_trees=30, # Number of trees grown\n", 747 | " X_features_fraction=0.75\n", 748 | " )\n", 749 | "\n", 750 | "# Growing the random forest \n", 751 | "rf.grow_random_forest()" 752 | ] 753 | }, 754 | { 755 | "cell_type": "code", 756 | "execution_count": 13, 757 | "metadata": { 758 | "scrolled": false 759 | }, 760 | "outputs": [], 761 | "source": [ 762 | "# Printing out the trees\n", 763 | "if rf.n_trees < 10:\n", 764 | " rf.print_trees()" 765 | ] 766 | }, 767 | { 768 | "cell_type": "markdown", 769 | "metadata": {}, 770 | "source": [ 771 | "# Predictions" 772 | ] 773 | }, 774 | { 775 | "cell_type": "code", 776 | "execution_count": 14, 777 | "metadata": { 778 | "scrolled": true 779 | }, 780 | "outputs": [ 781 | { 782 | "name": "stdout", 783 | "output_type": "stream", 784 | "text": [ 785 | "Total churns in test set: 116\n", 786 | "Total predicted churns in test set: 44\n", 787 | "Precision: 75.0 %\n", 788 | "Recall: 28.000000000000004 %\n" 789 | ] 790 | } 791 | ], 792 | "source": [ 793 | "yhat = rf.predict(test[features])\n", 794 | "test['yhat'] = yhat\n", 795 | "\n", 796 | "print(f\"Total churns in test set: {test['Churn'].sum()}\")\n", 797 | "print(f\"Total predicted churns in test set: {test['yhat'].sum()}\")\n", 798 | "\n", 799 | "print(f\"Precision: {round(precision_score(test['Churn'], test['yhat']), 2) * 100} %\")\n", 800 | "print(f\"Recall: {round(recall_score(test['Churn'], test['yhat']), 2) * 100} %\")" 801 | ] 802 | }, 803 | { 804 | "cell_type": "markdown", 805 | "metadata": {}, 806 | "source": [ 807 | "# Sklearn implementation " 808 | ] 809 | }, 810 | { 811 | "cell_type": "markdown", 812 | "metadata": {}, 813 | "source": [ 814 | "We can compare the custom implementation of RF to that of skicit learn. " 815 | ] 816 | }, 817 | { 818 | "cell_type": "code", 819 | "execution_count": 15, 820 | "metadata": {}, 821 | "outputs": [ 822 | { 823 | "name": "stdout", 824 | "output_type": "stream", 825 | "text": [ 826 | "Time took for scikit learn: 0.05 seconds\n", 827 | "Total churns in test set: 116\n", 828 | "Total predicted churns in test set: 44\n", 829 | "Precision: 79.0 %\n", 830 | "Recall: 26.0 %\n" 831 | ] 832 | } 833 | ], 834 | "source": [ 835 | "# Skicit learn implementation\n", 836 | "from sklearn.ensemble import RandomForestClassifier as RandomForestClassifierScikit\n", 837 | "\n", 838 | "# Initiating\n", 839 | "rf_scikit = RandomForestClassifierScikit(n_estimators=30, max_features=0.75, max_depth=3, min_samples_split=5)\n", 840 | "\n", 841 | "# Fitting \n", 842 | "start = time.time()\n", 843 | "rf_scikit.fit(X=train[features], y=train['Churn'])\n", 844 | "print(f\"Time took for scikit learn: {round(time.time() - start, 2)} seconds\")\n", 845 | "\n", 846 | "# Forecasting \n", 847 | "yhatsc = rf_scikit.predict(test[features])\n", 848 | "test['yhatsc'] = yhatsc\n", 849 | "\n", 850 | "print(f\"Total churns in test set: {test['Churn'].sum()}\")\n", 851 | "print(f\"Total predicted churns in test set: {test['yhat'].sum()}\")\n", 852 | "\n", 853 | "print(f\"Precision: {round(precision_score(test['Churn'], test['yhatsc']), 2) * 100} %\")\n", 854 | "print(f\"Recall: {round(recall_score(test['Churn'], test['yhatsc']), 2) * 100} %\")" 855 | ] 856 | }, 857 | { 858 | "cell_type": "code", 859 | "execution_count": null, 860 | "metadata": {}, 861 | "outputs": [], 862 | "source": [] 863 | } 864 | ], 865 | "metadata": { 866 | "kernelspec": { 867 | "display_name": "decision-tree", 868 | "language": "python", 869 | "name": "decision-tree" 870 | }, 871 | "language_info": { 872 | "codemirror_mode": { 873 | "name": "ipython", 874 | "version": 3 875 | }, 876 | "file_extension": ".py", 877 | "mimetype": "text/x-python", 878 | "name": "python", 879 | "nbconvert_exporter": "python", 880 | "pygments_lexer": "ipython3", 881 | "version": "3.8.8" 882 | } 883 | }, 884 | "nbformat": 4, 885 | "nbformat_minor": 4 886 | } 887 | -------------------------------------------------------------------------------- /RegressionDecisionTree.py: -------------------------------------------------------------------------------- 1 | # Data wrangling 2 | import pandas as pd 3 | 4 | # Array math 5 | import numpy as np 6 | 7 | # Quick value count calculator 8 | from collections import Counter 9 | 10 | 11 | class NodeRegression(): 12 | """ 13 | Class to grow a regression decision tree 14 | """ 15 | def __init__( 16 | self, 17 | Y: list, 18 | X: pd.DataFrame, 19 | min_samples_split=None, 20 | max_depth=None, 21 | depth=None, 22 | node_type=None, 23 | rule=None 24 | ): 25 | # Saving the data to the node 26 | self.Y = Y 27 | self.X = X 28 | 29 | # Saving the hyper parameters 30 | self.min_samples_split = min_samples_split if min_samples_split else 20 31 | self.max_depth = max_depth if max_depth else 5 32 | 33 | # Default current depth of node 34 | self.depth = depth if depth else 0 35 | 36 | # Extracting all the features 37 | self.features = list(self.X.columns) 38 | 39 | # Type of node 40 | self.node_type = node_type if node_type else 'root' 41 | 42 | # Rule for spliting 43 | self.rule = rule if rule else "" 44 | 45 | # Getting the mean of Y 46 | self.ymean = np.mean(Y) 47 | 48 | # Getting the residuals 49 | self.residuals = self.Y - self.ymean 50 | 51 | # Calculating the mse of the node 52 | self.mse = self.get_mse(Y, self.ymean) 53 | 54 | # Saving the number of observations in the node 55 | self.n = len(Y) 56 | 57 | # Initiating the left and right nodes as empty nodes 58 | self.left = None 59 | self.right = None 60 | 61 | # Default values for splits 62 | self.best_feature = None 63 | self.best_value = None 64 | 65 | @staticmethod 66 | def get_mse(ytrue, yhat) -> float: 67 | """ 68 | Method to calculate the mean squared error 69 | """ 70 | # Getting the total number of samples 71 | n = len(ytrue) 72 | 73 | # Getting the residuals 74 | r = ytrue - yhat 75 | 76 | # Squering the residuals 77 | r = r ** 2 78 | 79 | # Suming 80 | r = np.sum(r) 81 | 82 | # Getting the average and returning 83 | return r / n 84 | 85 | @staticmethod 86 | def ma(x: np.array, window: int) -> np.array: 87 | """ 88 | Calculates the moving average of the given list. 89 | """ 90 | return np.convolve(x, np.ones(window), 'valid') / window 91 | 92 | def best_split(self) -> tuple: 93 | """ 94 | Given the X features and Y targets calculates the best split 95 | for a decision tree 96 | """ 97 | # Creating a dataset for spliting 98 | df = self.X.copy() 99 | df['Y'] = self.Y 100 | 101 | # Getting the GINI impurity for the base input 102 | mse_base = self.mse 103 | 104 | # Finding which split yields the best GINI gain 105 | #max_gain = 0 106 | 107 | # Default best feature and split 108 | best_feature = None 109 | best_value = None 110 | 111 | for feature in self.features: 112 | # Droping missing values 113 | Xdf = df.dropna().sort_values(feature) 114 | 115 | # Sorting the values and getting the rolling average 116 | xmeans = self.ma(Xdf[feature].unique(), 2) 117 | 118 | for value in xmeans: 119 | # Getting the left and right ys 120 | left_y = Xdf[Xdf[feature]=value]['Y'].values 122 | 123 | # Getting the means 124 | left_mean = np.mean(left_y) 125 | right_mean = np.mean(right_y) 126 | 127 | # Getting the left and right residuals 128 | res_left = left_y - left_mean 129 | res_right = right_y - right_mean 130 | 131 | # Concatenating the residuals 132 | r = np.concatenate((res_left, res_right), axis=None) 133 | 134 | # Calculating the mse 135 | n = len(r) 136 | r = r ** 2 137 | r = np.sum(r) 138 | mse_split = r / n 139 | 140 | # Checking if this is the best split so far 141 | if mse_split < mse_base: 142 | best_feature = feature 143 | best_value = value 144 | 145 | # Setting the best gain to the current one 146 | mse_base = mse_split 147 | 148 | return (best_feature, best_value) 149 | 150 | def grow_tree(self): 151 | """ 152 | Recursive method to create the decision tree 153 | """ 154 | # Making a df from the data 155 | df = self.X.copy() 156 | df['Y'] = self.Y 157 | 158 | # If there is GINI to be gained, we split further 159 | if (self.depth < self.max_depth) and (self.n >= self.min_samples_split): 160 | 161 | # Getting the best split 162 | best_feature, best_value = self.best_split() 163 | 164 | if best_feature is not None: 165 | # Saving the best split to the current node 166 | self.best_feature = best_feature 167 | self.best_value = best_value 168 | 169 | # Getting the left and right nodes 170 | left_df, right_df = df[df[best_feature]<=best_value].copy(), df[df[best_feature]>best_value].copy() 171 | 172 | # Creating the left and right nodes 173 | left = NodeRegression( 174 | left_df['Y'].values.tolist(), 175 | left_df[self.features], 176 | depth=self.depth + 1, 177 | max_depth=self.max_depth, 178 | min_samples_split=self.min_samples_split, 179 | node_type='left_node', 180 | rule=f"{best_feature} <= {round(best_value, 3)}" 181 | ) 182 | 183 | self.left = left 184 | self.left.grow_tree() 185 | 186 | right = NodeRegression( 187 | right_df['Y'].values.tolist(), 188 | right_df[self.features], 189 | depth=self.depth + 1, 190 | max_depth=self.max_depth, 191 | min_samples_split=self.min_samples_split, 192 | node_type='right_node', 193 | rule=f"{best_feature} > {round(best_value, 3)}" 194 | ) 195 | 196 | self.right = right 197 | self.right.grow_tree() 198 | 199 | def print_info(self, width=4): 200 | """ 201 | Method to print the infromation about the tree 202 | """ 203 | # Defining the number of spaces 204 | const = int(self.depth * width ** 1.5) 205 | spaces = "-" * const 206 | 207 | if self.node_type == 'root': 208 | print("Root") 209 | else: 210 | print(f"|{spaces} Split rule: {self.rule}") 211 | print(f"{' ' * const} | MSE of the node: {round(self.mse, 2)}") 212 | print(f"{' ' * const} | Count of observations in node: {self.n}") 213 | print(f"{' ' * const} | Prediction of node: {round(self.ymean, 3)}") 214 | 215 | def print_tree(self): 216 | """ 217 | Prints the whole tree from the current node to the bottom 218 | """ 219 | self.print_info() 220 | 221 | if self.left is not None: 222 | self.left.print_tree() 223 | 224 | if self.right is not None: 225 | self.right.print_tree() 226 | 227 | if __name__ == '__main__': 228 | d = pd.read_csv("data/regression/auto-mpg.csv") 229 | 230 | # Subsetting 231 | d = d[d['horsepower']!='?'] 232 | 233 | # Constructing the X and Y matrices 234 | features = ['horsepower', 'weight', 'acceleration'] 235 | 236 | for ft in features: 237 | d[ft] = pd.to_numeric(d[ft]) 238 | 239 | X = d[features] 240 | Y = d['mpg'].values.tolist() 241 | 242 | # Initiating the Node 243 | root = NodeRegression(Y, X, max_depth=3, min_samples_split=3) 244 | 245 | # Growing the tree 246 | root.grow_tree() 247 | 248 | # Printing tree 249 | root.print_tree() -------------------------------------------------------------------------------- /ScikitLearnCompare.py: -------------------------------------------------------------------------------- 1 | # Importing the ML package 2 | from sklearn.tree import DecisionTreeClassifier, export_text, DecisionTreeRegressor 3 | 4 | # Importing the custom created class 5 | from DecisionTree import Node 6 | 7 | # Importing the custom regression tree 8 | from RegressionDecisionTree import NodeRegression 9 | 10 | # Data reading 11 | import pandas as pd 12 | 13 | # Array math 14 | import numpy as np 15 | 16 | # Reading the data 17 | d = pd.read_csv("data/classification/train.csv")[['Age', 'Fare', 'Survived']].dropna() 18 | 19 | # Constructing the X and Y matrices 20 | X = d[['Age', 'Fare']] 21 | Y = d['Survived'].values.tolist() 22 | 23 | # Constructing the parameter dict 24 | hp = { 25 | 'max_depth': 4, 26 | 'min_samples_split': 50 27 | } 28 | 29 | # Initiating the Node 30 | root = Node(Y, X, **hp) 31 | 32 | # Getting teh best split 33 | root.grow_tree() 34 | 35 | # Using the ML package 36 | clf = DecisionTreeClassifier(**hp) 37 | clf.fit(X, Y) 38 | 39 | # Printing out the trees 40 | root.print_tree() 41 | print(export_text(clf, feature_names=['Age', 'Fare'])) 42 | 43 | # Predictions 44 | X['scikit_learn'] = clf.predict(X[['Age', 'Fare']]) 45 | X['custom_yhat'] = root.predict(X[['Age', 'Fare']]) 46 | 47 | # Asserting that every prediction is the same 48 | np.all(X['scikit_learn'] == X['custom_yhat']) 49 | 50 | print(X[X['scikit_learn'] != X['custom_yhat']]) 51 | 52 | # Trying out regression 53 | # Reading the data 54 | d = pd.read_csv("data/regression/auto-mpg.csv") 55 | 56 | # Subsetting 57 | d = d[d['horsepower']!='?'] 58 | 59 | # Constructing the X and Y matrices 60 | features = ['horsepower', 'weight', 'acceleration'] 61 | 62 | for ft in features: 63 | d[ft] = pd.to_numeric(d[ft]) 64 | 65 | X = d[features] 66 | Y = d['mpg'].values.tolist() 67 | 68 | # Constructing the parameter dict 69 | hp = { 70 | 'max_depth': 4, 71 | 'min_samples_split': 10 72 | } 73 | 74 | # Initiating the Node 75 | root = NodeRegression(Y, X, **hp) 76 | 77 | # Getting teh best split 78 | root.grow_tree() 79 | 80 | # Using the ML package 81 | clf = DecisionTreeRegressor(**hp) 82 | clf.fit(X, Y) 83 | 84 | # Printing out the trees 85 | root.print_tree() 86 | print(export_text(clf, feature_names=X.columns.values.tolist())) -------------------------------------------------------------------------------- /data/classification/test.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q 9 | 899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S 10 | 900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C 11 | 901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S 12 | 902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S 13 | 903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S 14 | 904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S 15 | 905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S 16 | 906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S 17 | 907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C 18 | 908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q 19 | 909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C 20 | 910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S 21 | 911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C 22 | 912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C 23 | 913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S 24 | 914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S 25 | 915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C 26 | 916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C 27 | 917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S 28 | 918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C 29 | 919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C 30 | 920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S 31 | 921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C 32 | 922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S 33 | 923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S 34 | 924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S 35 | 925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S 36 | 926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C 37 | 927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C 38 | 928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S 39 | 929,3,"Cacic, Miss. 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challenger se 13 | 14,8,340,160,3609,8,70,1,plymouth 'cuda 340 14 | 15,8,400,150,3761,9.5,70,1,chevrolet monte carlo 15 | 14,8,455,225,3086,10,70,1,buick estate wagon (sw) 16 | 24,4,113,95,2372,15,70,3,toyota corona mark ii 17 | 22,6,198,95,2833,15.5,70,1,plymouth duster 18 | 18,6,199,97,2774,15.5,70,1,amc hornet 19 | 21,6,200,85,2587,16,70,1,ford maverick 20 | 27,4,97,88,2130,14.5,70,3,datsun pl510 21 | 26,4,97,46,1835,20.5,70,2,volkswagen 1131 deluxe sedan 22 | 25,4,110,87,2672,17.5,70,2,peugeot 504 23 | 24,4,107,90,2430,14.5,70,2,audi 100 ls 24 | 25,4,104,95,2375,17.5,70,2,saab 99e 25 | 26,4,121,113,2234,12.5,70,2,bmw 2002 26 | 21,6,199,90,2648,15,70,1,amc gremlin 27 | 10,8,360,215,4615,14,70,1,ford f250 28 | 10,8,307,200,4376,15,70,1,chevy c20 29 | 11,8,318,210,4382,13.5,70,1,dodge d200 30 | 9,8,304,193,4732,18.5,70,1,hi 1200d 31 | 27,4,97,88,2130,14.5,71,3,datsun pl510 32 | 28,4,140,90,2264,15.5,71,1,chevrolet vega 2300 33 | 25,4,113,95,2228,14,71,3,toyota corona 34 | 25,4,98,?,2046,19,71,1,ford pinto 35 | 19,6,232,100,2634,13,71,1,amc gremlin 36 | 16,6,225,105,3439,15.5,71,1,plymouth satellite custom 37 | 17,6,250,100,3329,15.5,71,1,chevrolet chevelle malibu 38 | 19,6,250,88,3302,15.5,71,1,ford torino 500 39 | 18,6,232,100,3288,15.5,71,1,amc matador 40 | 14,8,350,165,4209,12,71,1,chevrolet impala 41 | 14,8,400,175,4464,11.5,71,1,pontiac catalina brougham 42 | 14,8,351,153,4154,13.5,71,1,ford galaxie 500 43 | 14,8,318,150,4096,13,71,1,plymouth fury iii 44 | 12,8,383,180,4955,11.5,71,1,dodge monaco (sw) 45 | 13,8,400,170,4746,12,71,1,ford country squire (sw) 46 | 13,8,400,175,5140,12,71,1,pontiac safari (sw) 47 | 18,6,258,110,2962,13.5,71,1,amc hornet sportabout (sw) 48 | 22,4,140,72,2408,19,71,1,chevrolet vega (sw) 49 | 19,6,250,100,3282,15,71,1,pontiac firebird 50 | 18,6,250,88,3139,14.5,71,1,ford mustang 51 | 23,4,122,86,2220,14,71,1,mercury capri 2000 52 | 28,4,116,90,2123,14,71,2,opel 1900 53 | 30,4,79,70,2074,19.5,71,2,peugeot 304 54 | 30,4,88,76,2065,14.5,71,2,fiat 124b 55 | 31,4,71,65,1773,19,71,3,toyota corolla 1200 56 | 35,4,72,69,1613,18,71,3,datsun 1200 57 | 27,4,97,60,1834,19,71,2,volkswagen model 111 58 | 26,4,91,70,1955,20.5,71,1,plymouth cricket 59 | 24,4,113,95,2278,15.5,72,3,toyota corona hardtop 60 | 25,4,97.5,80,2126,17,72,1,dodge colt hardtop 61 | 23,4,97,54,2254,23.5,72,2,volkswagen type 3 62 | 20,4,140,90,2408,19.5,72,1,chevrolet vega 63 | 21,4,122,86,2226,16.5,72,1,ford pinto runabout 64 | 13,8,350,165,4274,12,72,1,chevrolet impala 65 | 14,8,400,175,4385,12,72,1,pontiac catalina 66 | 15,8,318,150,4135,13.5,72,1,plymouth fury iii 67 | 14,8,351,153,4129,13,72,1,ford galaxie 500 68 | 17,8,304,150,3672,11.5,72,1,amc ambassador sst 69 | 11,8,429,208,4633,11,72,1,mercury marquis 70 | 13,8,350,155,4502,13.5,72,1,buick lesabre custom 71 | 12,8,350,160,4456,13.5,72,1,oldsmobile delta 88 royale 72 | 13,8,400,190,4422,12.5,72,1,chrysler newport royal 73 | 19,3,70,97,2330,13.5,72,3,mazda rx2 coupe 74 | 15,8,304,150,3892,12.5,72,1,amc matador (sw) 75 | 13,8,307,130,4098,14,72,1,chevrolet chevelle concours (sw) 76 | 13,8,302,140,4294,16,72,1,ford gran torino (sw) 77 | 14,8,318,150,4077,14,72,1,plymouth satellite custom (sw) 78 | 18,4,121,112,2933,14.5,72,2,volvo 145e (sw) 79 | 22,4,121,76,2511,18,72,2,volkswagen 411 (sw) 80 | 21,4,120,87,2979,19.5,72,2,peugeot 504 (sw) 81 | 26,4,96,69,2189,18,72,2,renault 12 (sw) 82 | 22,4,122,86,2395,16,72,1,ford pinto (sw) 83 | 28,4,97,92,2288,17,72,3,datsun 510 (sw) 84 | 23,4,120,97,2506,14.5,72,3,toyouta corona mark ii (sw) 85 | 28,4,98,80,2164,15,72,1,dodge colt (sw) 86 | 27,4,97,88,2100,16.5,72,3,toyota corolla 1600 (sw) 87 | 13,8,350,175,4100,13,73,1,buick century 350 88 | 14,8,304,150,3672,11.5,73,1,amc matador 89 | 13,8,350,145,3988,13,73,1,chevrolet malibu 90 | 14,8,302,137,4042,14.5,73,1,ford gran torino 91 | 15,8,318,150,3777,12.5,73,1,dodge coronet custom 92 | 12,8,429,198,4952,11.5,73,1,mercury marquis brougham 93 | 13,8,400,150,4464,12,73,1,chevrolet caprice classic 94 | 13,8,351,158,4363,13,73,1,ford ltd 95 | 14,8,318,150,4237,14.5,73,1,plymouth fury gran sedan 96 | 13,8,440,215,4735,11,73,1,chrysler new yorker brougham 97 | 12,8,455,225,4951,11,73,1,buick electra 225 custom 98 | 13,8,360,175,3821,11,73,1,amc ambassador brougham 99 | 18,6,225,105,3121,16.5,73,1,plymouth valiant 100 | 16,6,250,100,3278,18,73,1,chevrolet nova custom 101 | 18,6,232,100,2945,16,73,1,amc hornet 102 | 18,6,250,88,3021,16.5,73,1,ford maverick 103 | 23,6,198,95,2904,16,73,1,plymouth duster 104 | 26,4,97,46,1950,21,73,2,volkswagen super beetle 105 | 11,8,400,150,4997,14,73,1,chevrolet impala 106 | 12,8,400,167,4906,12.5,73,1,ford country 107 | 13,8,360,170,4654,13,73,1,plymouth custom suburb 108 | 12,8,350,180,4499,12.5,73,1,oldsmobile vista cruiser 109 | 18,6,232,100,2789,15,73,1,amc gremlin 110 | 20,4,97,88,2279,19,73,3,toyota carina 111 | 21,4,140,72,2401,19.5,73,1,chevrolet vega 112 | 22,4,108,94,2379,16.5,73,3,datsun 610 113 | 18,3,70,90,2124,13.5,73,3,maxda rx3 114 | 19,4,122,85,2310,18.5,73,1,ford pinto 115 | 21,6,155,107,2472,14,73,1,mercury capri v6 116 | 26,4,98,90,2265,15.5,73,2,fiat 124 sport coupe 117 | 15,8,350,145,4082,13,73,1,chevrolet monte carlo s 118 | 16,8,400,230,4278,9.5,73,1,pontiac grand prix 119 | 29,4,68,49,1867,19.5,73,2,fiat 128 120 | 24,4,116,75,2158,15.5,73,2,opel manta 121 | 20,4,114,91,2582,14,73,2,audi 100ls 122 | 19,4,121,112,2868,15.5,73,2,volvo 144ea 123 | 15,8,318,150,3399,11,73,1,dodge dart custom 124 | 24,4,121,110,2660,14,73,2,saab 99le 125 | 20,6,156,122,2807,13.5,73,3,toyota mark ii 126 | 11,8,350,180,3664,11,73,1,oldsmobile omega 127 | 20,6,198,95,3102,16.5,74,1,plymouth duster 128 | 21,6,200,?,2875,17,74,1,ford maverick 129 | 19,6,232,100,2901,16,74,1,amc hornet 130 | 15,6,250,100,3336,17,74,1,chevrolet nova 131 | 31,4,79,67,1950,19,74,3,datsun b210 132 | 26,4,122,80,2451,16.5,74,1,ford pinto 133 | 32,4,71,65,1836,21,74,3,toyota corolla 1200 134 | 25,4,140,75,2542,17,74,1,chevrolet vega 135 | 16,6,250,100,3781,17,74,1,chevrolet chevelle malibu classic 136 | 16,6,258,110,3632,18,74,1,amc matador 137 | 18,6,225,105,3613,16.5,74,1,plymouth satellite sebring 138 | 16,8,302,140,4141,14,74,1,ford gran torino 139 | 13,8,350,150,4699,14.5,74,1,buick century luxus (sw) 140 | 14,8,318,150,4457,13.5,74,1,dodge coronet custom (sw) 141 | 14,8,302,140,4638,16,74,1,ford gran torino (sw) 142 | 14,8,304,150,4257,15.5,74,1,amc matador (sw) 143 | 29,4,98,83,2219,16.5,74,2,audi fox 144 | 26,4,79,67,1963,15.5,74,2,volkswagen dasher 145 | 26,4,97,78,2300,14.5,74,2,opel manta 146 | 31,4,76,52,1649,16.5,74,3,toyota corona 147 | 32,4,83,61,2003,19,74,3,datsun 710 148 | 28,4,90,75,2125,14.5,74,1,dodge colt 149 | 24,4,90,75,2108,15.5,74,2,fiat 128 150 | 26,4,116,75,2246,14,74,2,fiat 124 tc 151 | 24,4,120,97,2489,15,74,3,honda civic 152 | 26,4,108,93,2391,15.5,74,3,subaru 153 | 31,4,79,67,2000,16,74,2,fiat x1.9 154 | 19,6,225,95,3264,16,75,1,plymouth valiant custom 155 | 18,6,250,105,3459,16,75,1,chevrolet nova 156 | 15,6,250,72,3432,21,75,1,mercury monarch 157 | 15,6,250,72,3158,19.5,75,1,ford maverick 158 | 16,8,400,170,4668,11.5,75,1,pontiac catalina 159 | 15,8,350,145,4440,14,75,1,chevrolet bel air 160 | 16,8,318,150,4498,14.5,75,1,plymouth grand fury 161 | 14,8,351,148,4657,13.5,75,1,ford ltd 162 | 17,6,231,110,3907,21,75,1,buick century 163 | 16,6,250,105,3897,18.5,75,1,chevroelt chevelle malibu 164 | 15,6,258,110,3730,19,75,1,amc matador 165 | 18,6,225,95,3785,19,75,1,plymouth fury 166 | 21,6,231,110,3039,15,75,1,buick skyhawk 167 | 20,8,262,110,3221,13.5,75,1,chevrolet monza 2+2 168 | 13,8,302,129,3169,12,75,1,ford mustang ii 169 | 29,4,97,75,2171,16,75,3,toyota corolla 170 | 23,4,140,83,2639,17,75,1,ford pinto 171 | 20,6,232,100,2914,16,75,1,amc gremlin 172 | 23,4,140,78,2592,18.5,75,1,pontiac astro 173 | 24,4,134,96,2702,13.5,75,3,toyota corona 174 | 25,4,90,71,2223,16.5,75,2,volkswagen dasher 175 | 24,4,119,97,2545,17,75,3,datsun 710 176 | 18,6,171,97,2984,14.5,75,1,ford pinto 177 | 29,4,90,70,1937,14,75,2,volkswagen rabbit 178 | 19,6,232,90,3211,17,75,1,amc pacer 179 | 23,4,115,95,2694,15,75,2,audi 100ls 180 | 23,4,120,88,2957,17,75,2,peugeot 504 181 | 22,4,121,98,2945,14.5,75,2,volvo 244dl 182 | 25,4,121,115,2671,13.5,75,2,saab 99le 183 | 33,4,91,53,1795,17.5,75,3,honda civic cvcc 184 | 28,4,107,86,2464,15.5,76,2,fiat 131 185 | 25,4,116,81,2220,16.9,76,2,opel 1900 186 | 25,4,140,92,2572,14.9,76,1,capri ii 187 | 26,4,98,79,2255,17.7,76,1,dodge colt 188 | 27,4,101,83,2202,15.3,76,2,renault 12tl 189 | 17.5,8,305,140,4215,13,76,1,chevrolet chevelle malibu classic 190 | 16,8,318,150,4190,13,76,1,dodge coronet brougham 191 | 15.5,8,304,120,3962,13.9,76,1,amc matador 192 | 14.5,8,351,152,4215,12.8,76,1,ford gran torino 193 | 22,6,225,100,3233,15.4,76,1,plymouth valiant 194 | 22,6,250,105,3353,14.5,76,1,chevrolet nova 195 | 24,6,200,81,3012,17.6,76,1,ford maverick 196 | 22.5,6,232,90,3085,17.6,76,1,amc hornet 197 | 29,4,85,52,2035,22.2,76,1,chevrolet chevette 198 | 24.5,4,98,60,2164,22.1,76,1,chevrolet woody 199 | 29,4,90,70,1937,14.2,76,2,vw rabbit 200 | 33,4,91,53,1795,17.4,76,3,honda civic 201 | 20,6,225,100,3651,17.7,76,1,dodge aspen se 202 | 18,6,250,78,3574,21,76,1,ford granada ghia 203 | 18.5,6,250,110,3645,16.2,76,1,pontiac ventura sj 204 | 17.5,6,258,95,3193,17.8,76,1,amc pacer d/l 205 | 29.5,4,97,71,1825,12.2,76,2,volkswagen rabbit 206 | 32,4,85,70,1990,17,76,3,datsun b-210 207 | 28,4,97,75,2155,16.4,76,3,toyota corolla 208 | 26.5,4,140,72,2565,13.6,76,1,ford pinto 209 | 20,4,130,102,3150,15.7,76,2,volvo 245 210 | 13,8,318,150,3940,13.2,76,1,plymouth volare premier v8 211 | 19,4,120,88,3270,21.9,76,2,peugeot 504 212 | 19,6,156,108,2930,15.5,76,3,toyota mark ii 213 | 16.5,6,168,120,3820,16.7,76,2,mercedes-benz 280s 214 | 16.5,8,350,180,4380,12.1,76,1,cadillac seville 215 | 13,8,350,145,4055,12,76,1,chevy c10 216 | 13,8,302,130,3870,15,76,1,ford f108 217 | 13,8,318,150,3755,14,76,1,dodge d100 218 | 31.5,4,98,68,2045,18.5,77,3,honda accord cvcc 219 | 30,4,111,80,2155,14.8,77,1,buick opel isuzu deluxe 220 | 36,4,79,58,1825,18.6,77,2,renault 5 gtl 221 | 25.5,4,122,96,2300,15.5,77,1,plymouth arrow gs 222 | 33.5,4,85,70,1945,16.8,77,3,datsun f-10 hatchback 223 | 17.5,8,305,145,3880,12.5,77,1,chevrolet caprice classic 224 | 17,8,260,110,4060,19,77,1,oldsmobile cutlass supreme 225 | 15.5,8,318,145,4140,13.7,77,1,dodge monaco brougham 226 | 15,8,302,130,4295,14.9,77,1,mercury cougar brougham 227 | 17.5,6,250,110,3520,16.4,77,1,chevrolet concours 228 | 20.5,6,231,105,3425,16.9,77,1,buick skylark 229 | 19,6,225,100,3630,17.7,77,1,plymouth volare custom 230 | 18.5,6,250,98,3525,19,77,1,ford granada 231 | 16,8,400,180,4220,11.1,77,1,pontiac grand prix lj 232 | 15.5,8,350,170,4165,11.4,77,1,chevrolet monte carlo landau 233 | 15.5,8,400,190,4325,12.2,77,1,chrysler cordoba 234 | 16,8,351,149,4335,14.5,77,1,ford thunderbird 235 | 29,4,97,78,1940,14.5,77,2,volkswagen rabbit custom 236 | 24.5,4,151,88,2740,16,77,1,pontiac sunbird coupe 237 | 26,4,97,75,2265,18.2,77,3,toyota corolla liftback 238 | 25.5,4,140,89,2755,15.8,77,1,ford mustang ii 2+2 239 | 30.5,4,98,63,2051,17,77,1,chevrolet chevette 240 | 33.5,4,98,83,2075,15.9,77,1,dodge colt m/m 241 | 30,4,97,67,1985,16.4,77,3,subaru dl 242 | 30.5,4,97,78,2190,14.1,77,2,volkswagen dasher 243 | 22,6,146,97,2815,14.5,77,3,datsun 810 244 | 21.5,4,121,110,2600,12.8,77,2,bmw 320i 245 | 21.5,3,80,110,2720,13.5,77,3,mazda rx-4 246 | 43.1,4,90,48,1985,21.5,78,2,volkswagen rabbit custom diesel 247 | 36.1,4,98,66,1800,14.4,78,1,ford fiesta 248 | 32.8,4,78,52,1985,19.4,78,3,mazda glc deluxe 249 | 39.4,4,85,70,2070,18.6,78,3,datsun b210 gx 250 | 36.1,4,91,60,1800,16.4,78,3,honda civic cvcc 251 | 19.9,8,260,110,3365,15.5,78,1,oldsmobile cutlass salon brougham 252 | 19.4,8,318,140,3735,13.2,78,1,dodge diplomat 253 | 20.2,8,302,139,3570,12.8,78,1,mercury monarch ghia 254 | 19.2,6,231,105,3535,19.2,78,1,pontiac phoenix lj 255 | 20.5,6,200,95,3155,18.2,78,1,chevrolet malibu 256 | 20.2,6,200,85,2965,15.8,78,1,ford fairmont (auto) 257 | 25.1,4,140,88,2720,15.4,78,1,ford fairmont (man) 258 | 20.5,6,225,100,3430,17.2,78,1,plymouth volare 259 | 19.4,6,232,90,3210,17.2,78,1,amc concord 260 | 20.6,6,231,105,3380,15.8,78,1,buick century special 261 | 20.8,6,200,85,3070,16.7,78,1,mercury zephyr 262 | 18.6,6,225,110,3620,18.7,78,1,dodge aspen 263 | 18.1,6,258,120,3410,15.1,78,1,amc concord d/l 264 | 19.2,8,305,145,3425,13.2,78,1,chevrolet monte carlo landau 265 | 17.7,6,231,165,3445,13.4,78,1,buick regal sport coupe (turbo) 266 | 18.1,8,302,139,3205,11.2,78,1,ford futura 267 | 17.5,8,318,140,4080,13.7,78,1,dodge magnum xe 268 | 30,4,98,68,2155,16.5,78,1,chevrolet chevette 269 | 27.5,4,134,95,2560,14.2,78,3,toyota corona 270 | 27.2,4,119,97,2300,14.7,78,3,datsun 510 271 | 30.9,4,105,75,2230,14.5,78,1,dodge omni 272 | 21.1,4,134,95,2515,14.8,78,3,toyota celica gt liftback 273 | 23.2,4,156,105,2745,16.7,78,1,plymouth sapporo 274 | 23.8,4,151,85,2855,17.6,78,1,oldsmobile starfire sx 275 | 23.9,4,119,97,2405,14.9,78,3,datsun 200-sx 276 | 20.3,5,131,103,2830,15.9,78,2,audi 5000 277 | 17,6,163,125,3140,13.6,78,2,volvo 264gl 278 | 21.6,4,121,115,2795,15.7,78,2,saab 99gle 279 | 16.2,6,163,133,3410,15.8,78,2,peugeot 604sl 280 | 31.5,4,89,71,1990,14.9,78,2,volkswagen scirocco 281 | 29.5,4,98,68,2135,16.6,78,3,honda accord lx 282 | 21.5,6,231,115,3245,15.4,79,1,pontiac lemans v6 283 | 19.8,6,200,85,2990,18.2,79,1,mercury zephyr 6 284 | 22.3,4,140,88,2890,17.3,79,1,ford fairmont 4 285 | 20.2,6,232,90,3265,18.2,79,1,amc concord dl 6 286 | 20.6,6,225,110,3360,16.6,79,1,dodge aspen 6 287 | 17,8,305,130,3840,15.4,79,1,chevrolet caprice classic 288 | 17.6,8,302,129,3725,13.4,79,1,ford ltd landau 289 | 16.5,8,351,138,3955,13.2,79,1,mercury grand marquis 290 | 18.2,8,318,135,3830,15.2,79,1,dodge st. regis 291 | 16.9,8,350,155,4360,14.9,79,1,buick estate wagon (sw) 292 | 15.5,8,351,142,4054,14.3,79,1,ford country squire (sw) 293 | 19.2,8,267,125,3605,15,79,1,chevrolet malibu classic (sw) 294 | 18.5,8,360,150,3940,13,79,1,chrysler lebaron town @ country (sw) 295 | 31.9,4,89,71,1925,14,79,2,vw rabbit custom 296 | 34.1,4,86,65,1975,15.2,79,3,maxda glc deluxe 297 | 35.7,4,98,80,1915,14.4,79,1,dodge colt hatchback custom 298 | 27.4,4,121,80,2670,15,79,1,amc spirit dl 299 | 25.4,5,183,77,3530,20.1,79,2,mercedes benz 300d 300 | 23,8,350,125,3900,17.4,79,1,cadillac eldorado 301 | 27.2,4,141,71,3190,24.8,79,2,peugeot 504 302 | 23.9,8,260,90,3420,22.2,79,1,oldsmobile cutlass salon brougham 303 | 34.2,4,105,70,2200,13.2,79,1,plymouth horizon 304 | 34.5,4,105,70,2150,14.9,79,1,plymouth horizon tc3 305 | 31.8,4,85,65,2020,19.2,79,3,datsun 210 306 | 37.3,4,91,69,2130,14.7,79,2,fiat strada custom 307 | 28.4,4,151,90,2670,16,79,1,buick skylark limited 308 | 28.8,6,173,115,2595,11.3,79,1,chevrolet citation 309 | 26.8,6,173,115,2700,12.9,79,1,oldsmobile omega brougham 310 | 33.5,4,151,90,2556,13.2,79,1,pontiac phoenix 311 | 41.5,4,98,76,2144,14.7,80,2,vw rabbit 312 | 38.1,4,89,60,1968,18.8,80,3,toyota corolla tercel 313 | 32.1,4,98,70,2120,15.5,80,1,chevrolet chevette 314 | 37.2,4,86,65,2019,16.4,80,3,datsun 310 315 | 28,4,151,90,2678,16.5,80,1,chevrolet citation 316 | 26.4,4,140,88,2870,18.1,80,1,ford fairmont 317 | 24.3,4,151,90,3003,20.1,80,1,amc concord 318 | 19.1,6,225,90,3381,18.7,80,1,dodge aspen 319 | 34.3,4,97,78,2188,15.8,80,2,audi 4000 320 | 29.8,4,134,90,2711,15.5,80,3,toyota corona liftback 321 | 31.3,4,120,75,2542,17.5,80,3,mazda 626 322 | 37,4,119,92,2434,15,80,3,datsun 510 hatchback 323 | 32.2,4,108,75,2265,15.2,80,3,toyota corolla 324 | 46.6,4,86,65,2110,17.9,80,3,mazda glc 325 | 27.9,4,156,105,2800,14.4,80,1,dodge colt 326 | 40.8,4,85,65,2110,19.2,80,3,datsun 210 327 | 44.3,4,90,48,2085,21.7,80,2,vw rabbit c (diesel) 328 | 43.4,4,90,48,2335,23.7,80,2,vw dasher (diesel) 329 | 36.4,5,121,67,2950,19.9,80,2,audi 5000s (diesel) 330 | 30,4,146,67,3250,21.8,80,2,mercedes-benz 240d 331 | 44.6,4,91,67,1850,13.8,80,3,honda civic 1500 gl 332 | 40.9,4,85,?,1835,17.3,80,2,renault lecar deluxe 333 | 33.8,4,97,67,2145,18,80,3,subaru dl 334 | 29.8,4,89,62,1845,15.3,80,2,vokswagen rabbit 335 | 32.7,6,168,132,2910,11.4,80,3,datsun 280-zx 336 | 23.7,3,70,100,2420,12.5,80,3,mazda rx-7 gs 337 | 35,4,122,88,2500,15.1,80,2,triumph tr7 coupe 338 | 23.6,4,140,?,2905,14.3,80,1,ford mustang cobra 339 | 32.4,4,107,72,2290,17,80,3,honda accord 340 | 27.2,4,135,84,2490,15.7,81,1,plymouth reliant 341 | 26.6,4,151,84,2635,16.4,81,1,buick skylark 342 | 25.8,4,156,92,2620,14.4,81,1,dodge aries wagon (sw) 343 | 23.5,6,173,110,2725,12.6,81,1,chevrolet citation 344 | 30,4,135,84,2385,12.9,81,1,plymouth reliant 345 | 39.1,4,79,58,1755,16.9,81,3,toyota starlet 346 | 39,4,86,64,1875,16.4,81,1,plymouth champ 347 | 35.1,4,81,60,1760,16.1,81,3,honda civic 1300 348 | 32.3,4,97,67,2065,17.8,81,3,subaru 349 | 37,4,85,65,1975,19.4,81,3,datsun 210 mpg 350 | 37.7,4,89,62,2050,17.3,81,3,toyota tercel 351 | 34.1,4,91,68,1985,16,81,3,mazda glc 4 352 | 34.7,4,105,63,2215,14.9,81,1,plymouth horizon 4 353 | 34.4,4,98,65,2045,16.2,81,1,ford escort 4w 354 | 29.9,4,98,65,2380,20.7,81,1,ford escort 2h 355 | 33,4,105,74,2190,14.2,81,2,volkswagen jetta 356 | 34.5,4,100,?,2320,15.8,81,2,renault 18i 357 | 33.7,4,107,75,2210,14.4,81,3,honda prelude 358 | 32.4,4,108,75,2350,16.8,81,3,toyota corolla 359 | 32.9,4,119,100,2615,14.8,81,3,datsun 200sx 360 | 31.6,4,120,74,2635,18.3,81,3,mazda 626 361 | 28.1,4,141,80,3230,20.4,81,2,peugeot 505s turbo diesel 362 | 30.7,6,145,76,3160,19.6,81,2,volvo diesel 363 | 25.4,6,168,116,2900,12.6,81,3,toyota cressida 364 | 24.2,6,146,120,2930,13.8,81,3,datsun 810 maxima 365 | 22.4,6,231,110,3415,15.8,81,1,buick century 366 | 26.6,8,350,105,3725,19,81,1,oldsmobile cutlass ls 367 | 20.2,6,200,88,3060,17.1,81,1,ford granada gl 368 | 17.6,6,225,85,3465,16.6,81,1,chrysler lebaron salon 369 | 28,4,112,88,2605,19.6,82,1,chevrolet cavalier 370 | 27,4,112,88,2640,18.6,82,1,chevrolet cavalier wagon 371 | 34,4,112,88,2395,18,82,1,chevrolet cavalier 2-door 372 | 31,4,112,85,2575,16.2,82,1,pontiac j2000 se hatchback 373 | 29,4,135,84,2525,16,82,1,dodge aries se 374 | 27,4,151,90,2735,18,82,1,pontiac phoenix 375 | 24,4,140,92,2865,16.4,82,1,ford fairmont futura 376 | 23,4,151,?,3035,20.5,82,1,amc concord dl 377 | 36,4,105,74,1980,15.3,82,2,volkswagen rabbit l 378 | 37,4,91,68,2025,18.2,82,3,mazda glc custom l 379 | 31,4,91,68,1970,17.6,82,3,mazda glc custom 380 | 38,4,105,63,2125,14.7,82,1,plymouth horizon miser 381 | 36,4,98,70,2125,17.3,82,1,mercury lynx l 382 | 36,4,120,88,2160,14.5,82,3,nissan stanza xe 383 | 36,4,107,75,2205,14.5,82,3,honda accord 384 | 34,4,108,70,2245,16.9,82,3,toyota corolla 385 | 38,4,91,67,1965,15,82,3,honda civic 386 | 32,4,91,67,1965,15.7,82,3,honda civic (auto) 387 | 38,4,91,67,1995,16.2,82,3,datsun 310 gx 388 | 25,6,181,110,2945,16.4,82,1,buick century limited 389 | 38,6,262,85,3015,17,82,1,oldsmobile cutlass ciera (diesel) 390 | 26,4,156,92,2585,14.5,82,1,chrysler lebaron medallion 391 | 22,6,232,112,2835,14.7,82,1,ford granada l 392 | 32,4,144,96,2665,13.9,82,3,toyota celica gt 393 | 36,4,135,84,2370,13,82,1,dodge charger 2.2 394 | 27,4,151,90,2950,17.3,82,1,chevrolet camaro 395 | 27,4,140,86,2790,15.6,82,1,ford mustang gl 396 | 44,4,97,52,2130,24.6,82,2,vw pickup 397 | 32,4,135,84,2295,11.6,82,1,dodge rampage 398 | 28,4,120,79,2625,18.6,82,1,ford ranger 399 | 31,4,119,82,2720,19.4,82,1,chevy s-10 400 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | pandas==1.2.3 2 | scikit-learn==0.24.1 3 | jupyter==1.0.0 4 | matplotlib==3.4.0 5 | pyaml==20.4.0 6 | seaborn==0.11.1 --------------------------------------------------------------------------------