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https://github.com/udhaya2823/cardheko-used_car_price_prediction

🚗 Car Dheko - Used Car Price Prediction This project enhances Car Dheko's customer experience by deploying an ML model that predicts used car prices accurately. Using a multi-city dataset, we perform data cleaning, feature engineering, and model optimization. The final model is hosted on a Streamlit app, providing instant price prediction.
https://github.com/udhaya2823/cardheko-used_car_price_prediction

data-cleaning-and-preprocessing documentation-and-reporting exploratory-data-analysis machine-learning-model-deployment model-deployment model-evaluation-and-optimization price-prediction-techniques streamlit-application-development

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🚗 Car Dheko - Used Car Price Prediction This project enhances Car Dheko's customer experience by deploying an ML model that predicts used car prices accurately. Using a multi-city dataset, we perform data cleaning, feature engineering, and model optimization. The final model is hosted on a Streamlit app, providing instant price prediction.

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# 🚗 **Car Dheko - Used Car Price Prediction**



> **Accurate and Interactive Tool for Estimating Used Car Prices**  

> Using Data Science, Machine Learning, and Streamlit



![Project Banner](https://img.shields.io/badge/Machine%20Learning-Price%20Prediction-brightgreen) ![Project Status](https://img.shields.io/badge/Status-Completed-blue) ![Technologies](https://img.shields.io/badge/Tech-Python%20%7C%20Pandas%20%7C%20Streamlit%20%7C%20Scikit--learn%20-brightblue)



This project enhances Car Dheko's customer experience by deploying a streamlined ML model to predict used car prices accurately. Leveraging a dataset with historical prices across multiple cities, we perform data cleaning, feature engineering, and model optimization to deliver reliable predictions. The final model is deployed as a user-friendly Streamlit application, allowing users to get real-time price estimates with ease.



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## 🌟 **Project Overview**



**Objective:**  

Transform customer interactions and streamline pricing decisions by building a machine learning model that predicts used car prices based on detailed car features.



**Scope:**  

Analyze data from multiple cities with features such as car make, model, year, mileage, fuel type, and transmission. The end goal is to deploy a tool that predicts prices accurately based on these attributes and is accessible to both customers and sales representatives through an interactive web app.



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## 🧰 **Skills and Tech Stack**



| Skill Area                  | Description                                  |

|-----------------------------|----------------------------------------------|

| Data Cleaning & Preprocessing | Handling missing values, scaling, and encoding |

| Exploratory Data Analysis (EDA) | Understanding data distribution and feature importance |

| Machine Learning             | Model development, training, tuning |

| Model Evaluation             | Comparing MAE, MSE, R-Squared metrics |

| Streamlit Application        | Deploying the model in a user-friendly interface |

| Documentation                | Comprehensive reporting and project summary |



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## 📑 **Dataset**



- **Source:** Car Dheko data, spanning multiple cities with features like make, model, year, fuel type, transmission type, etc.

- **Structure:** Structured data format with columns representing car features and target prices.



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## 🛠 **Project Workflow**



### 1. **Data Processing**

   - **Concatenation**: Combine multiple city datasets into one structured dataset.

   - **Missing Value Handling**: Use imputation methods for both numerical and categorical data.

   - **Standardization**: Normalize and clean data (e.g., converting units, handling categorical values).



### 2. **Exploratory Data Analysis (EDA)**

   - **Visualization**: Identify patterns and trends.

   - **Feature Selection**: Analyze key features impacting car prices.

  

### 3. **Model Development**

   - **Algorithms**: Train various regression models like Linear Regression, Random Forest, and Gradient Boosting.

   - **Hyperparameter Tuning**: Use Grid Search for optimal parameters.



### 4. **Model Evaluation and Optimization**

   - **Metrics**: MAE, MSE, and R-Squared.

   - **Feature Engineering**: Enhance model accuracy with feature adjustments.



### 5. **Deployment**

   - **Streamlit Application**: Provides real-time price prediction based on user input.

   - **UI Design**: Interactive, easy-to-use interface for customers and sales teams.



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## 🎨 **Streamlit Application**



The app features:

   - **Simple User Inputs**: Enter car details like make, model, year, etc.

   - **Instant Prediction**: Real-time price predictions.

   - **User-Friendly Design**: Intuitive and responsive interface.



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## 📊 **Results and Deliverables**



- **ML Model**: Accurate prediction model with high performance on test data.

- **Interactive App**: User-friendly tool for estimating car prices.

- **Documentation**: Clear explanation of methodology, data processing steps, and results.



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## 💡 **Project Insights**



- Leveraging features like car age, mileage, and condition significantly impacted model performance.

- Data preprocessing played a key role in ensuring model accuracy by handling missing values and standardizing features.

- The Streamlit app allows users to make predictions effortlessly, improving usability and enhancing customer engagement.



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## 📄 **License**



This project is licensed under the MIT License.



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## 🌐 **Connect with Us**



For feedback, collaboration, or queries, reach out via:

- [LinkedIn](www.linkedin.com/in/udhaya-kumar-v-e23212405)



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> **Built with passion for Car Dheko by [Udhaya Kumar V]([https://github.com/udhaya2823])**