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BIG-data/**model.py**

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import pandas as pd | |

from sklearn.linear_model import LinearRegression | |

from sklearn.model_selection import train_test_split | |

from sklearn.metrics import mean_squared_error, r2_score | |

from sklearn.preprocessing import StandardScaler | |

import numpy as np | |

# Load the data | |

trips_full_data_df = pd.read_csv('Trips_Full Data.csv') | |

trips_by_distance_df = pd.read_csv('Trips_by_Distance.csv') | |

# Convert 'Date' to datetime if it's not already and set as index | |

trips_full_data_df['Date'] = pd.to_datetime(trips_full_data_df['Date']) | |

trips_by_distance_df['Date'] = pd.to_datetime(trips_by_distance_df['Date']) | |

# Set 'Date' as index to prepare for merge | |

trips_full_data_df.set_index('Date', inplace=True) | |

trips_by_distance_df.set_index('Date', inplace=True) | |

# Filter the data for Week 32 of 2019 for the predictor variables | |

week_32_full = trips_full_data_df[trips_full_data_df['Week of Date'] == 'Week 32'] | |

# Prepare the Week 31 data for the target variable and group by 'Date' | |

week_31_distance = trips_by_distance_df[trips_by_distance_df['Week'] == 31] | |

week_31_distance_grouped = week_31_distance.groupby('Date').agg({'Number of Trips 5-10': 'sum'}) | |

# Merge the Week 32 and Week 31 data on 'Date' | |

merged_data = week_32_full.join(week_31_distance_grouped) | |

if merged_data.isnull().values.any(): | |

print("Warning: NaN values found after merging. Check alignment of 'Date' columns.") | |

merged_data.dropna(inplace=True) | |

X = merged_data[['Trips 1-25 Miles', 'Trips 25-100 Miles']] | |

y = merged_data['Number of Trips 5-10'] | |

X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42) | |

scaler = StandardScaler() | |

X_train_scaled = scaler.fit_transform(X_train) | |

X_val_scaled = scaler.transform(X_val) | |

model = LinearRegression() | |

model.fit(X_train_scaled, y_train) | |

from sklearn.metrics import r2_score, mean_squared_error | |

# Predict on the validation set | |

y_pred = model.predict(X_val_scaled) | |

# Calculate R² on the validation set | |

r2 = r2_score(y_val, y_pred) | |

# Calculate RMSE on the validation set | |

mse = mean_squared_error(y_val, y_pred) | |

rmse = np.sqrt(mse) # Calculating the square root of the MSE to get RMSE | |

print(f"Coefficients: {model.coef_}") | |

print(f"Intercept: {model.intercept_}") | |

print(f"Linear Regression - Coefficient of determination (R^2) on validation set: {r2}") | |

print(f"Linear Regression - Root Mean Square Error (RMSE) on validation set: {rmse}") |