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.