Car Price Prediction

 Car Price Prediction

1. Introduction


Objective: Build a predictive model to estimate car prices based on various features such as brand, mileage, engine size, and age.
Purpose: Help users make informed decisions about buying or selling cars by predicting accurate prices.

2. Project Workflow


1. Problem Definition:
   - Predict car prices based on historical data and features.
   - Key questions:
     - Which features significantly influence car prices?
     - How can regression models accurately predict prices?
2. Data Collection:
   - Source: Datasets from car sales platforms or public repositories.
   - Example fields: Brand, Model, Year, Mileage, Engine Size, Fuel Type, Transmission, Price.
3. Data Preprocessing:
   - Handle missing values, outliers, and encode categorical variables.
4. Model Development:
   - Use regression techniques like Linear Regression or Random Forest Regressor.
5. Model Evaluation:
   - Assess model performance using metrics like R-squared and RMSE.

3. Technical Requirements


- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Data Visualization: Matplotlib, Seaborn
  - Regression Models: Scikit-learn
  - Model Evaluation: Scikit-learn

4. Implementation Steps

Step 1: Setup Environment


Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn
```

Step 2: Load and Explore Data


Load the car sales dataset:
```
import pandas as pd

data = pd.read_csv("car_data.csv")
print(data.head())
```
Explore key statistics and correlations:
```
print(data.describe())
print(data.corr())
```
Visualize feature relationships:
```
import seaborn as sns
sns.pairplot(data)
```

Step 3: Preprocess Data


Handle missing values and encode categorical data:
```
data = data.dropna()  # Drop rows with missing values

# Encode categorical variables
data = pd.get_dummies(data, columns=['Brand', 'FuelType', 'Transmission'], drop_first=True)
```
Normalize numerical features:
```
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaled_features = scaler.fit_transform(data[['Mileage', 'EngineSize', 'Age']])
data[['Mileage', 'EngineSize', 'Age']] = scaled_features
```

Step 4: Build Regression Model


Split the data into training and testing sets:
```
from sklearn.model_selection import train_test_split

X = data.drop('Price', axis=1)
y = data['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
Train a Linear Regression model:
```
from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
```
Predict on the test set:
```
y_pred = model.predict(X_test)
```

Step 5: Evaluate Model


Evaluate the model using R-squared and RMSE:
```
from sklearn.metrics import r2_score, mean_squared_error

r2 = r2_score(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)

print("R-squared:", r2)
print("RMSE:", rmse)
```

Step 6: Advanced Modeling (Optional)


Experiment with other regression techniques:
- Random Forest Regressor
- Gradient Boosting Regressor
Example:
```
from sklearn.ensemble import RandomForestRegressor

rf_model = RandomForestRegressor(random_state=42)
rf_model.fit(X_train, y_train)
rf_pred = rf_model.predict(X_test)
```

5. Expected Outcomes


1. A trained regression model capable of predicting car prices.
2. Insights into the most influential features affecting car prices.
3. Model evaluation metrics for assessing prediction accuracy.

6. Additional Suggestions


- Deployment:
  - Develop a user-friendly web app to predict car prices based on input features.
  - Use frameworks like Flask or Streamlit for deployment.
- Feature Engineering:
  - Create new features such as price per mile or depreciation rate for better insights.
- Regular Updates:
  - Retrain the model periodically with new data for improved accuracy.