{"id":31698721,"url":"https://github.com/satyas567/weatherdataanalysis","last_synced_at":"2026-04-02T03:03:03.563Z","repository":{"id":318291637,"uuid":"1070656580","full_name":"satyaS567/WeatherDataAnalysis","owner":"satyaS567","description":"Comprehensive Weather Data Analysis with Python: Explore trends, visualize patterns, detect outliers, and predict temperature using humidity and wind speed","archived":false,"fork":false,"pushed_at":"2025-10-06T09:13:35.000Z","size":3675,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-10-06T11:23:20.395Z","etag":null,"topics":["jupyter-notebook","linear-regression","matplotlib","numpy","pandas","python","scikit-learn","seaborn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/satyaS567.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-10-06T09:02:07.000Z","updated_at":"2025-10-06T09:16:28.000Z","dependencies_parsed_at":"2025-10-06T11:33:29.125Z","dependency_job_id":null,"html_url":"https://github.com/satyaS567/WeatherDataAnalysis","commit_stats":null,"previous_names":["satyas567/weatherdataanalysis"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/satyaS567/WeatherDataAnalysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satyaS567%2FWeatherDataAnalysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satyaS567%2FWeatherDataAnalysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satyaS567%2FWeatherDataAnalysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satyaS567%2FWeatherDataAnalysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/satyaS567","download_url":"https://codeload.github.com/satyaS567/WeatherDataAnalysis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/satyaS567%2FWeatherDataAnalysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279000701,"owners_count":26082805,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["jupyter-notebook","linear-regression","matplotlib","numpy","pandas","python","scikit-learn","seaborn"],"created_at":"2025-10-08T19:10:24.118Z","updated_at":"2025-10-08T19:10:26.327Z","avatar_url":"https://github.com/satyaS567.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"🌦️ Weather Data Analysis Project\n\n\nThis project provides an in-depth Exploratory Data Analysis (EDA) and predictive modeling on a weather dataset using Python.\nIt includes data cleaning, preprocessing, visualization, statistical insights, and linear regression modeling to uncover meaningful weather patterns and relationships between variables like temperature, humidity, and wind speed.\n\n🧰 Technologies Used\n\n  1. Python 3.x\n\n  2. Libraries:\n\n  3. pandas – data manipulation\n\n  4. numpy – numerical computations\n\n  5. matplotlib, seaborn – data visualization\n\n  6. scipy.stats – statistical analysis\n\n  7. scikit-learn – machine learning model (Linear Regression)\n\n🚀 Key Steps in the Project\n\n1️⃣ Data Loading \u0026 Inspection\n\n  Loads dataset (weatherHistory.csv)\n\n  Displays shape, columns, missing values, and summary statistics\n\n2️⃣ Data Cleaning \u0026 Preprocessing\n\n  Renames inconsistent column headers\n\n  Converts date column to datetime\n\n  Removes duplicates and missing timestamps\n\n  Ensures numeric data types are consistent\n\n3️⃣ Feature Engineering\n\n  Extracts Year, Month, Day, and Hour\n\n  Creates a Season feature based on month\n\n4️⃣ Exploratory Data Analysis (EDA)\n\n  Distribution plots for Temperature and Humidity\n\n  Relationship between temperature and humidity\n\n  Correlation heatmap for feature relationships\n\n  Seasonal boxplots and weather summaries\n\n5️⃣ Time Series Analysis\n\n  Monthly and daily temperature trends\n\n  Seasonal variations visualized using line charts\n\n6️⃣ Outlier Detection \u0026 Statistical Insights\n\n  Identifies temperature outliers using Z-Score\n\n  Calculates Skewness, Kurtosis, and correlation metrics\n\n7️⃣ Predictive Modeling\n\n  Builds a Linear Regression model using:\n\n  Independent Variables: Humidity, Wind_Speed_km_h\n\n  Target Variable: Temperature_C\n\n  Evaluates model using:\n\n  R² Score\n\n  RMSE (Root Mean Squared Error)\n\n8️⃣ Key Insights\n\n  ✅ Temperature shows strong seasonal variation (warmest mid-year)\n  \n  ✅ Humidity has a negative correlation with temperature\n  \n  ✅ Wind speed shows weak correlation with temperature\n  \n  ✅ Model gives moderate accuracy in temperature prediction\n\n📊 Example Visualizations\n\n  Temperature Distribution\n\n  Humidity Distribution\n\n  Temperature vs Humidity Scatterplot\n\n  Correlation Heatmap\n\n  Monthly Temperature Trend\n\n  Seasonal Boxplot\n\n  (Plots generated using Matplotlib and Seaborn)\n\n⚙️ How to Run the Project\n\n🔧 1. Clone this repository\n\n  git clone https://github.com/satyaS567/WeatherDataAnalysis.git\n\n  cd WeatherDataAnalysis\n\n📦 2. Install dependencies\n  pip install -r requirements.txt\n\n▶️ 3. Run the script\n  python weather_analysis.py\n\n📈 Sample Model Output\n\n--- Linear Regression Model ---\n\nR² Score: 0.742\n\nRMSE: 1.86\n\nCoefficients: {'Humidity': -12.57, 'Wind_Speed_km_h': 0.22}\n\n🎯 Conclusion\n\nThis project demonstrates how data analysis and machine learning can uncover trends in weather patterns.\nIt highlights seasonal changes, humidity-temperature relationships, and builds a simple predictive model to forecast temperature using climatic parameters.\n\n🧠 Future Enhancements\n\nApply advanced regression models (Random Forest, XGBoost)\n\nAdd temperature anomaly detection\n\nBuild an interactive dashboard using Plotly or Power BI\n\nDeploy model as a Flask web app for live prediction\n\n👨‍💻 Author\n\nSatya Prakash Sharma\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatyas567%2Fweatherdataanalysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsatyas567%2Fweatherdataanalysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsatyas567%2Fweatherdataanalysis/lists"}