Car Price Prediction Project Using Python and Machine Learning

In this project, I developed a predictive model to estimate car prices using machine learning techniques, leveraging various Python libraries such as Pandas, Matplotlib, Seaborn, and Scikit-learn. This project demonstrates the process of data handling, model training, and performance evaluation in a real-world context. 1. Data Collection and Exploration The project begins by loading the data from a CSV file using the Pandas library, a powerful tool for data management and analysis. I then explored the dataset to understand the available features, such as car name, selling price, fuel type, seller type, and transmission type. 2. Data Preprocessing The next step involved preprocessing the data to prepare it for modeling. I checked for missing values and encoded categorical features (like fuel type, seller type, and transmission type) into numerical values using Pandas 3. Data Splitting To ensure the model’s effectiveness, I split the data into training and testing sets using train_test_split from sklearn.model_selection. This split helps verify that the model can accurately predict on data it hasn’t seen before. 4. Model Training I used the LinearRegression model from Sklearn to train the model on the training data. Additionally, I experimented with the Lasso model to control complexity and prevent overfitting. 5. Prediction and Performance Evaluation After training the model, I made predictions on both the training and testing data. To evaluate the model’s performance, I used the R-squared metric from metrics to measure the model's accuracy. I also used Matplotlib and Seaborn to visually present the prediction results.