How thatipramod/codealpha_tasks works
The repository contains two independent Streamlit data-science products. Unemployment Analysis cleans regional time-series data and presents trend, COVID-19, seasonal, and regional views. Sales Prediction explores advertising data, compares regression models, and offers interactive predictions and model analytics. The sales dashboard retrains through model_utils.py; sales_prediction.py separately writes a model artifact. Unemployment source excerpts are partial, so only visible data flow and README-documented analysis capabilities are represented.
Main entry points: app.py Dashboard entry · model_utils.py Load and train · analysis.py Clean and analyze · sales_prediction.py Prepare and compare
Components 20
Unemployment Analysis
- Raw dataset
CodeAlpha_UnemploymentAnalysis/data/raw/Unemployment in India.csv - Clean and analyze
CodeAlpha_UnemploymentAnalysis/analysis.py - Cleaned dataset
CodeAlpha_UnemploymentAnalysis/data/cleaned/unemployment_cleaned.csv - Regional summary
CodeAlpha_UnemploymentAnalysis/outputs/regional_summary.csv - Analytics dashboard
CodeAlpha_UnemploymentAnalysis/app.py
Sales Prediction
- Advertising dataset
CodeAlpha_sales_prediction/Advertising.csv - Prepare and compare
CodeAlpha_sales_prediction/sales_prediction.py - Cleaned sales data
CodeAlpha_sales_prediction/cleaned_data.csv - Saved model
CodeAlpha_sales_prediction/models/sales_prediction_model.pkl - Dashboard entry
CodeAlpha_sales_prediction/app.py - Load and train
CodeAlpha_sales_prediction/model_utils.py - Selected regressor
CodeAlpha_sales_prediction/model_utils.py
Sales Dashboard Pages
- Home overview
CodeAlpha_sales_prediction/app.py - Sales prediction
CodeAlpha_sales_prediction/pages/predict_sales.py - Data insights
CodeAlpha_sales_prediction/pages/Data_Insights.py - Model performance
CodeAlpha_sales_prediction/pages/Model_Performance.py - Feature importance
CodeAlpha_sales_prediction/pages/Feature_Importance.py - Project information
CodeAlpha_sales_prediction/pages/About.py
Other components
- Analyst
- Sales user
Connections 23
- Analyst → Analytics dashboard: explores
- Raw dataset → Clean and analyze: loads and cleans
- Clean and analyze → Cleaned dataset: writes cleaned data
- Clean and analyze → Regional summary: writes summary
- Cleaned dataset → Analytics dashboard: dashboard reads
- Sales user → Dashboard entry: uses
- Advertising dataset → Prepare and compare: loads
- Prepare and compare → Cleaned sales data: writes cleaned data
- Prepare and compare → Saved model: saves model
- Dashboard entry → Home overview: dispatches page
- Dashboard entry → Sales prediction: dispatches page
- Dashboard entry → Data insights: dispatches page
- Dashboard entry → Model performance: dispatches page
- Dashboard entry → Feature importance: dispatches page
- Dashboard entry → Project information: dispatches page
- Dashboard entry → Load and train: loads data and trains
- Sales prediction → Load and train: loads data and trains
- Data insights → Load and train: loads data and trains
- Model performance → Load and train: loads data and trains
- Feature importance → Load and train: loads data and trains
- Load and train → Cleaned sales data: reads data
- Load and train → Selected regressor: returns selected model
- Sales prediction → Selected regressor: predicts sales