Added readme file to car price prediction using Machine Learning

Car Price Prediction Using Machine Learning/Readme.md

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+# Car Price Prediction Using Machine Learning 🚗
+
+🔹This project aims to predict the price of car using Machine Learning algorithms.
+
+🔹This aim of the project is to estimate the price of a car based on various features such as make, model, engine size, fuel type, and other specifications. 
+
+🔹The dataset can be obtained from kaggle.
+
+## Methodology 🔎
+
+🔹The Car Price Prediction project follows a structured approach to estimate price of the car. Key steps include:
+
+1. **Importing Required Libraries**: Import necessary libraries such as pandas, numpy, matplotlib, seaborn, and sklearn.
+2. **Loading the Dataset**: Load the dataset using pandas and display the first few rows to understand its structure.
+3. **Preprocess Data**: Normalize and encode categorical variables.
+4. **Split Data**: Split the data into training and test sets.
+5. **Train Machine Learning Models**: Train models such as Linear Regression, Random Forest Classifier, and Decision Tree.
+6. **Evaluate Models' Performance**: Evaluate the models using accuracy score, confusion matrix, and ROC curve.
+7. **Select Best Performing Model**: Choose the best performing model for further analysis.
+
+## Technologies Used 🚀
+
+1. **Python**: The primary programming language used for the project.
+2. **Pandas**: A powerful data manipulation and analysis library used for data cleaning, preparation, and exploration.
+3. **NumPy**: A library for numerical computations used for handling arrays and mathematical functions.
+4. **Matplotlib**: A plotting library used for creating static, animated, and interactive visualizations in Python.
+5. **Seaborn**: A data visualization library based on Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics.
+6. **Scikit-learn**: A machine learning library in Python used for building and evaluating machine learning models.
+
+## Features 💡
+
+1. **Data Preprocessing**: Normalize and encode data for better model performance.
+2.  **Model Training**: Train various machine learning models.
+3.  **Model Evaluation**: Evaluate models using multiple metrics.
+4.  **Data Visualization**: Visualize data and results using various plots.
+
+## Conclusion 📌
+
+🔹  This project evaluates used car price prediction using kaggle dataset which gives an accuracy of 96% for Random forest regression.
+
+🔹 The Random Forest was found to be the most effective models for predicting car prices showing strong performance on the testing data.
+
+🔹 This model will be helpful for customers to know the price of a similar car using a recommendation engine.
+
+