MachineLearning_Lab1

🚖 Taxi Fare Prediction - City of Chicago

This project aims to predict the total taxi fare in Chicago using features like trip duration, distance, tip amount, payment type, and taxi company. The dataset is taken from the City of Chicago Taxi Trips dataset.

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📂 Dataset

The dataset contains records of individual taxi rides in Chicago.
Used columns:

• Trip Miles
• Trip Seconds
• Tips
• Payment Type
• Company
• Trip Total (Target)

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🧠 Objective

Build a Linear Regression model to predict the Trip Total (fare) for a given trip using selected features.

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🛠 Tech Stack

• Python 🐍
• pandas, NumPy
• scikit-learn
• matplotlib

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🔍 Steps Followed

✅ Step 1: Load the dataset

Used pandas to load and inspect the dataset.

✅ Step 2: Select Features

Selected only the most relevant columns and removed rows with missing values.

✅ Step 3: Preprocessing

• Used OneHotEncoder to encode categorical columns (Payment Type, Company)
• Built a Pipeline to apply preprocessing + Linear Regression

✅ Step 4: Model Training & Evaluation

• Split the data into training and test sets (80/20)
• Trained a Linear Regression model
• Evaluated using:
  • Mean Absolute Error (MAE)
  • R² Score

✅ Step 5: Visualization

Plotted Actual vs Predicted Fare to visualize performance.

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📊 Sample Results