{"id":19491564,"url":"https://github.com/mastermindromii/car-price-prediction-model","last_synced_at":"2026-04-13T19:31:40.764Z","repository":{"id":227494622,"uuid":"771584136","full_name":"MasterMindRomii/Car-Price-Prediction-Model","owner":"MasterMindRomii","description":"Here is My Regression Project based on Predicting Price of Car using Linear Regression.","archived":false,"fork":false,"pushed_at":"2025-08-11T12:13:43.000Z","size":910,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-11T14:19:33.921Z","etag":null,"topics":["linear-regression","matplotlib","numpy","pandas","python","scikit-learn","seaborn"],"latest_commit_sha":null,"homepage":"https://car-price-prediction-model-mpr-project.streamlit.app/","language":"Jupyter 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unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["linear-regression","matplotlib","numpy","pandas","python","scikit-learn","seaborn"],"created_at":"2024-11-10T21:17:20.059Z","updated_at":"2026-04-13T19:31:40.755Z","avatar_url":"https://github.com/MasterMindRomii.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🚗 Car Price Prediction\n\nHello Everyone,  \n\nThis is my **Regression Project** aimed at predicting used car prices using **Linear Regression**.  \nIt demonstrates my skills in **data cleaning, visualization, feature engineering, and model building**.  \n\n---\n\n## 📊 Dataset\n\n**Source:** [Honda Used Car Selling](https://www.kaggle.com/datasets/themrityunjaypathak/honda-car-selling)  \n\nThe dataset contains various attributes of used cars, such as **model, fuel type, kilometers driven, suspension, and selling price**.\n\n---\n\n## 🎯 Problem Statement\n\nThe goal is to develop a **Machine Learning model** that can predict the price of a used car based on its features.  \nThis helps buyers and sellers make **data-driven** pricing decisions.\n\n---\n\n## 🛠 Tech Stack \u0026 Libraries\n\n```python\nimport numpy as np \nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_score\nfrom sklearn.linear_model import LinearRegression\n%matplotlib inline\n📂 Project Workflow\n\n1️⃣ Data Loading \u0026 Exploration\ndf = pd.read_csv(\"honda_car_selling.csv\")\ndf.head()\ndf.info()\ndf.shape\n\n2️⃣ Data Cleaning\nRemoved extra whitespaces from Fuel Type, Suspension, and Car Model.\n\nConverted kms driven into integers after stripping \"kms\".\n\nConverted price from \"6.45 Lakh\" to 645000 using a custom function.\n\ndf['Fuel_Type'] = df['Fuel_Type'].str.strip()\ndf['Suspension'] = df['Suspension'].str.strip()\ndf['Car_Model'] = df['Car_Model'].str.strip()\n\ndf['kms_driven'] = df['kms_driven'].str.split().str[0].astype(int)\n\ndef convert_price(price_str):\n    return int(float(price_str.split()[0]) * 100000)\n\ndf['Price'] = df['Price'].apply(convert_price)\n\n3️⃣ Data Visualization\nsns.swarmplot(x='Year', y='Price', data=df)\nsns.relplot(x='kms_driven', y='Price', data=df)\nsns.relplot(x='Car_Model', y='Price', hue='Suspension', data=df)\n\n4️⃣ Feature Engineering\ndf = pd.get_dummies(df, columns=['Fuel_Type', 'Suspension'], drop_first=True)\n\n5️⃣ Model Building \u0026 Evaluation\nX = df.drop('Price', axis=1)\ny = df['Price']\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\nmodel = LinearRegression()\nmodel.fit(X_train, y_train)\n\ncv = KFold(n_splits=10)\nscores = cross_val_score(model, X, y, cv=cv, scoring='r2')\nprint(\"Cross-validation scores:\", scores)\nprint(\"Mean R² score:\", scores.mean())\n\n📌 Conclusion\nDeveloped a Linear Regression Model to predict car prices based on multiple attributes.\n\nAchieved an average prediction accuracy of ~82%.\n\nValidated model performance using K-Fold Cross Validation with a mean R² score of ~83%.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmastermindromii%2Fcar-price-prediction-model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmastermindromii%2Fcar-price-prediction-model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmastermindromii%2Fcar-price-prediction-model/lists"}