https://github.com/batthulavinay/phone-usage-analysis
This project analyzes phone usage patterns in India and predicts the primary use of mobile devices based on various features. The notebook covers data preprocessing, exploratory data analysis (EDA), and model training using multiple classification algorithms.
https://github.com/batthulavinay/phone-usage-analysis
accuracy-score adaboost classification-report confusion-matrix datapreprocessing datavisualization exploratory-data-analysis gradient-boosting imbalanced-data jupyter-notebook lightgbm logistic-regression matplotlib numpy pandas random-forest scikit-learn seaborn supportvectormachines xgboost
Last synced: 2 months ago
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This project analyzes phone usage patterns in India and predicts the primary use of mobile devices based on various features. The notebook covers data preprocessing, exploratory data analysis (EDA), and model training using multiple classification algorithms.
- Host: GitHub
- URL: https://github.com/batthulavinay/phone-usage-analysis
- Owner: BatthulaVinay
- Created: 2025-01-25T18:19:49.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-12T09:25:58.000Z (4 months ago)
- Last Synced: 2025-02-12T10:36:38.132Z (4 months ago)
- Topics: accuracy-score, adaboost, classification-report, confusion-matrix, datapreprocessing, datavisualization, exploratory-data-analysis, gradient-boosting, imbalanced-data, jupyter-notebook, lightgbm, logistic-regression, matplotlib, numpy, pandas, random-forest, scikit-learn, seaborn, supportvectormachines, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 1.29 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# phone-usage-analysis
This project analyzes phone usage patterns in India and predicts the primary use of mobile devices based on various features. The notebook covers data preprocessing, exploratory data analysis (EDA), and model training using multiple classification algorithms.
## Features
The dataset contains the following features:
- **Categorical features:**
- Gender
- Location
- Phone Brand
- Operating System (OS)
- Primary Use
- **Numerical features:**
- Various usage metrics (unnamed in this draft)
## Workflow
### 1. Data Preprocessing
- Dropped irrelevant features (e.g., User ID).
- Label encoded categorical variables.
- Normalized numerical features using `MinMaxScaler`.
- Split data into training and testing sets (80/20 split).
### 2. Exploratory Data Analysis (EDA)
- Visualized distributions of categorical features.
- Explored relationships between usage patterns and the target variable (Primary Use).
- Analyzed numerical feature distributions and their relationships with categorical variables using violin plots.
### 3. Model Training and Evaluation
Implemented and evaluated the following classifiers:
- Random Forest
- AdaBoost
- Gradient Boosting
- Extra Trees
- Logistic Regression
- Support Vector Machine
- XGBoost
- LightGBM
#### Evaluation Metrics:
- Accuracy Score
- Confusion Matrix
- Classification Report
### 4. Visualization of Results
- Bar plots to compare model accuracy.
- Heatmaps for confusion matrices of each classifier.
## Setup Instructions
### Prerequisites
- Python 3.x
- Required libraries:
- [`numpy`](https://numpy.org/)
- [`pandas`](https://pandas.pydata.org/)
- [`matplotlib`](https://matplotlib.org/)
- [`seaborn`](https://seaborn.pydata.org/)
- [`scikit-learn`](https://scikit-learn.org/)
- [`imbalanced-learn`](https://imbalanced-learn.org/)
- [`xgboost`](https://xgboost.readthedocs.io/)
- [`lightgbm`](https://lightgbm.readthedocs.io/)
### Installation
1. Clone this repository.
2. Install the dependencies using:
```bash
pip install -r requirements.txt
```
### Running the Notebook
1. Ensure the dataset (`phone_usage_india.csv`) is in the project directory.
2. Open and run the Jupyter Notebook using:
```bash
Jupiter notebook "Phone Usage in India .ipynb"
```
## Results
The notebook provides:
- Insights into the primary uses of phones in India.
- Performance comparison of multiple machine learning models for predicting primary phone usage.
- Visualizations for better interpretability of results.
## Acknowledgments
- Dataset: The data was sourced from a study on phone usage patterns.
- Libraries: Special thanks to contributors of Python libraries used in this project.
## License
This project is licensed under the MIT License. See the LICENSE file for details.