LP3/ML/ML1.md

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
from math import radians, cos, sin, asin, sqrt


# Load the dataset
df = pd.read_csv("uber.csv")   # change to your local path if needed
print("Initial Data Shape:", df.shape)
print(df.head())


# Rename columns for easier reference
df.rename(columns={'pickup_datetime': 'pickup_datetime'}, inplace=True)


# Convert pickup_datetime to datetime object
df['pickup_datetime'] = pd.to_datetime(df['pickup_datetime'], errors='coerce')


# Extract useful datetime features
df['hour'] = df['pickup_datetime'].dt.hour
df['day'] = df['pickup_datetime'].dt.day
df['month'] = df['pickup_datetime'].dt.month
df['year'] = df['pickup_datetime'].dt.year
df['day_of_week'] = df['pickup_datetime'].dt.dayofweek


# Drop datetime column (not needed as a direct feature)
df.drop(['pickup_datetime', 'key'], axis=1, inplace=True, errors='ignore')

print("\nColumns after feature extraction:\n", df.columns)


# Remove entries with unrealistic fares
df = df[(df['fare_amount'] > 0) & (df['fare_amount'] < 100)]


# Remove unrealistic latitude and longitude values
df = df[(df['pickup_latitude'] <= 90) & (df['pickup_latitude'] >= -90)]
df = df[(df['dropoff_latitude'] <= 90) & (df['dropoff_latitude'] >= -90)]
df = df[(df['pickup_longitude'] <= 180) & (df['pickup_longitude'] >= -180)]
df = df[(df['dropoff_longitude'] <= 180) & (df['dropoff_longitude'] >= -180)]


print("Data shape after removing outliers:", df.shape)


# Define Haversine function to calculate distance between pickup and drop-off
def haversine(lat1, lon1, lat2, lon2):
    # convert decimal degrees to radians
    lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
    # haversine formula
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a))
    km = 6371 * c
    return km


#Apply the Haversine formula
df['distance_km'] = df.apply(lambda x: haversine(x['pickup_latitude'], x['pickup_longitude'],
                                                 x['dropoff_latitude'], x['dropoff_longitude']), axis=1)


# Remove zero-distance trips
df = df[df['distance_km'] > 0]


plt.figure(figsize=(10, 6))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')
plt.title("Feature Correlation Heatmap")
plt.show()


# Define features and target
X = df[['distance_km', 'hour', 'day', 'month', 'year', 'day_of_week']]
y = df['fare_amount']


# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)


# -------------------- Linear Regression --------------------
lr_model = LinearRegression()
lr_model.fit(X_train, y_train)
y_pred_lr = lr_model.predict(X_test)


# -------------------- Random Forest Regression --------------------
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
y_pred_rf = rf_model.predict(X_test)


def evaluate_model(y_true, y_pred, model_name):
    r2 = r2_score(y_true, y_pred)
    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
    mae = mean_absolute_error(y_true, y_pred)
    print(f"\nModel: {model_name}")
    print(f"R² Score: {r2:.4f}")
    print(f"RMSE: {rmse:.4f}")
    print(f"MAE: {mae:.4f}")
    return r2, rmse, mae

# Evaluate both models
lr_scores = evaluate_model(y_test, y_pred_lr, "Linear Regression")
rf_scores = evaluate_model(y_test, y_pred_rf, "Random Forest Regressor")


results = pd.DataFrame({
    'Model': ['Linear Regression', 'Random Forest Regressor'],
    'R2': [lr_scores[0], rf_scores[0]],
    'RMSE': [lr_scores[1], rf_scores[1]],
    'MAE': [lr_scores[2], rf_scores[2]]
})

print("\nModel Comparison:")
print(results)


# Plot comparison
plt.figure(figsize=(8,5))
sns.barplot(x='Model', y='R2', data=results)
plt.title("R² Score Comparison between Models")
plt.show()