Consumer Price Indexes Prediction

Consumer Index Prediction/Model/README.md

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+## Consumer Index Prediction
+
+### Introduction
+
+Predicting future consumer indices is crucial for economic forecasting and financial planning. This project explores various machine learning models for this task, including Random Forest, Decision Tree, Logistic Regression, and compares them to Deep Learning methods. Additionally, visualizations with Matplotlib and Seaborn enhance the analysis.
+
+### Technologies
+
+* Python 3.x
+* TensorFlow (Deep Learning)
+* Scikit-learn (Random Forest, Decision Tree, Logistic Regression)
+* Matplotlib (Visualization)
+* Seaborn (Statistical Visualization)
+* Pandas (Data Manipulation)
+* NumPy (Numerical Calculations)
+
+### Data
+
+The data is downloaded from the kaggle website (https://www.kaggle.com/datasets/satyampd/consumer-price-index-india-inflation-data).
+### Model Training and Evaluation
+
+1. **Data Preprocessing:** Clean, scale, and prepare the data for each model.
+2. **Model Implementation:**
+    * **Random Forest:** Train a Random Forest Regressor with hyperparameter tuning.
+    * **Decision Tree:** Train a Decision Tree Regressor with hyperparameter tuning.
+    * **Logistic Regression:** Train a Logistic Regression model (if predicting binary outcome).
+    * **Deep Learning (optional):** Build and train a Deep Neural Network for regression or classification.
+3. **Evaluation:** Compare model performance using metrics like Mean Squared Error (MSE), R-squared, or F1-score (depending on the task).
+4. **Visualization:**
+    * Plot actual vs. predicted consumer index values for each model.
+    * Visualize feature importance in Random Forest and Decision Tree.
+    * Analyze model residuals to identify potential biases.
+
+### Conclusion
+
+* While Deep Learning models often achieve high accuracy, simpler models like Random Forest and Decision Trees can be effective for consumer index prediction, especially with careful feature selection and hyperparameter tuning.
+* Logistic Regression can be suitable for predicting binary outcomes, such as whether the index will rise or fall.
+* Visualization plays a crucial role in understanding model behavior, identifying potential issues, and communicating insights.
+
+### Next Steps
+
+* Explore feature engineering and dimensionality reduction techniques.
+* Implement ensemble methods like stacking for improved performance.
+* Apply the models to real-world data and monitor their accuracy over time.

Consumer Index Prediction/requirements.txt

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+The python latest version needs to install (above 3.9)
+
+The library needs to install:-
+1. numpy 
+2. pandas
+3. matplotlib.pyplot 
+4. seaborn 
+5. tensorflow
+
+The Jupiter notebook needs to install by using anaconda or vscode for a better view.
+