Project 1: crude-oil-forecast

Project Overview

Goals

• Forecast Brent Crude Oil prices in Ghana using various models including ARIMA, SSA, Prophet, Random Forests, XG Boost

Problem Statement

• Predicting future crude oil prices to aid in economic planning and decision-making.

Data

• Source: Historical crude oil price data from Bank of Ghana
• Description: The dataset contains monthly crude oil prices in USD per Barrel.
• Features: Date, Price.

Methodology

• Data preprocessing, feature engineering, and model selection including ARIMA,SSA,Prophet, XGBoost, and Random Forest.

Results

• ARIMA, SSA,Prophet,Random Forest,and XG Boost models were evaluated, with Random Forest showing better performance.

Conclusion

• Random Forest model provided more accurate forecasts, suggesting its suitability for this task.

Code Structure

Directory Structure

crude-oil-forecast/
├── data/
│   ├── Commodity Prices Monthly.csv
│   ├── Modified_Data.csv
├── notebooks/
│   ├── arimav3.ipynb
│   ├── rf.ipynb
├── scripts/
│   ├── arima.py
│   ├── arimav2.py
│   ├── arima_full.py
├── tests/
│   ├── arima.py
├── readme.md

Code Explanation

• scripts/arima.py: Implements ARIMA model for forecasting.
• notebooks/rf.ipynb: Contains the implementation of the Random Forest model.
• tests/arima.py: Unit tests for ARIMA model functions.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • scikit-learn: 0.24.1
  • matplotlib: 3.3.4
  • statsmodels: 0.12.2

Model Details

Architecture

• ARIMA and Random Forest models.

Training

• Data split into training and test sets, hyperparameter tuning using GridSearchCV.

Evaluation

• Metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE).
• Results: Random Forest outperformed ARIMA in terms of lower MAE and RMSE.

Deployment

Deployment Strategy

• Not applicable for this project.

Infrastructure

• Not applicable for this project.

Future Work

• Explore additional models like LSTM for time series forecasting.
• Incorporate more features such as economic indicators.

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Project 2: [Telecel Churn Analysis]("/Telecel Churn Analysis")

Project Overview

Goals

• Predict customer churn on the Telecel network.

Problem Statement

• Identifying customers likely to churn to improve retention strategies.

Data

• Source: Customer data from Telecel network.
• Description: The dataset contains customer information and churn labels.
• Features: Customer demographics, usage patterns, churn labels.

Methodology

• Data preprocessing, feature engineering, and model selection including Logistic Regression, Random Forest, and Gradient Boosting.

Results

• Random Forest and Gradient Boosting models showed high accuracy in predicting churn.

Conclusion

• The models can effectively predict churn, aiding in targeted retention efforts.

Code Structure

Directory Structure

telecel/
├── data/
│   ├── telecel_data.csv
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── data_preprocessing.ipynb
│   ├── model_development.ipynb
├── models/
├── requirements.txt
├── readme.md

Code Explanation

• notebooks/data_exploration.ipynb: Exploratory data analysis.
• notebooks/data_preprocessing.ipynb: Data cleaning and preprocessing.
• notebooks/model_development.ipynb: Model training and evaluation.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • scikit-learn: 0.24.1
  • matplotlib: 3.3.4
  • seaborn: 0.11.1

Model Details

Architecture

• Logistic Regression, Random Forest, Gradient Boosting.

Training

• Data split into training and test sets, hyperparameter tuning using RandomizedSearchCV.

Evaluation

• Metrics: Accuracy, Precision, Recall, F1-Score.
• Results: Random Forest and Gradient Boosting achieved high accuracy and F1-Score.

Deployment

Deployment Strategy

• Streamlit app for model deployment.

Infrastructure

• Hosted on a cloud platform with Streamlit.

Future Work

• Implement additional models like XGBoost.
• Enhance feature engineering with more customer behavior data.

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Project 3: [Monetary Policy Rate Analysis]("/Monetary Policy Rate Analysis")

Project Overview

Goals

• Predict the average policy rate of Ghana based on economic factors.

Problem Statement

• Forecasting policy rates to aid in economic planning and decision-making.

Data

• Source: Economic indicators such as GDP, unemployment, and inflation.
• Description: The dataset contains historical economic data.
• Features: GDP, Unemployment Rate, Inflation Rate, Policy Rate.

Methodology

• Data preprocessing, feature engineering, and model selection including Linear Regression and Random Forest.

Results

• Random Forest model provided accurate predictions of policy rates.

Conclusion

• The model can be used for economic forecasting and policymaking.

Code Structure

Directory Structure

mpr/
├── data/
│   ├── economic_data.csv
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── preprocessing.py
│   ├── models.ipynb
├── requirements.txt
├── readme.md

Code Explanation

• notebooks/data_exploration.ipynb: Exploratory data analysis.
• notebooks/preprocessing.py: Data cleaning and preprocessing.
• notebooks/models.ipynb: Model training and evaluation.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • scikit-learn: 0.24.1
  • matplotlib: 3.3.4
  • seaborn: 0.11.1

Model Details

Architecture

• Linear Regression, Random Forest.

Training

• Data split into training and test sets, hyperparameter tuning using GridSearchCV.

Evaluation

• Metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE).
• Results: Random Forest outperformed Linear Regression in terms of lower MAE and RMSE.

Deployment

Deployment Strategy

• Not applicable for this project.

Infrastructure

• Not applicable for this project.

Future Work

• Explore additional models like XGBoost.
• Incorporate more features such as international economic indicators.

---

Target Audience: Data scientists.

Style Guide: Use consistent formatting and clear headings for readability.

Code Snippets: Include relevant code snippets to illustrate key points.

Visualizations: Use visualizations to enhance understanding.

Version Control: Use version control for the documentation to track changes and updates.

---

This template can be adapted for other projects in your workspace by following the same structure and filling in the relevant details.

jnopareboateng: @workspace please continue for the remaining projects

Workspace: Sure, here is the continuation of the documentation for the remaining projects in your workspace:

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Project 4: msft-mlflow

Project Overview

Goals

• Implement and track machine learning experiments using MLflow.

Problem Statement

• Efficiently manage and track machine learning experiments to ensure reproducibility and scalability.

Data

• Source: Various datasets used for different machine learning tasks.
• Description: The datasets vary depending on the specific experiment being tracked.
• Features: Varies by dataset.

Methodology

• Use MLflow to track experiments, log parameters, metrics, and artifacts.

Results

• Improved experiment tracking and reproducibility.

Conclusion

• MLflow provides a robust framework for managing machine learning experiments.

Code Structure

Directory Structure

msft-mlflow/
├── data/
├── notebooks/
├── scripts/
├── models/
├── requirements.txt
├── readme.md

Code Explanation

• notebooks/: Contains Jupyter Notebooks for different experiments.
• scripts/: Python scripts for data preprocessing, model training, and evaluation.
• models/: Directory to save trained models.
• requirements.txt: Lists the required Python packages.

Dependencies

• Python: 3.8
• Libraries:
  • mlflow: 1.14.0
  • pandas: 1.2.3
  • scikit-learn: 0.24.1
  • matplotlib: 3.3.4

Model Details

Architecture

• Various models depending on the experiment.

Training

• Data split into training and test sets, hyperparameter tuning using GridSearchCV.

Evaluation

• Metrics: Varies by experiment.
• Results: Logged in MLflow.

Deployment

Deployment Strategy

• Not applicable for this project.

Infrastructure

• Not applicable for this project.

Future Work

• Integrate with other tools like TensorBoard for enhanced visualization.
• Explore automated hyperparameter tuning.

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Project 5: [Stock Returns Analysis]("/Stock Returns Analysis")

Project Overview

Goals

• Predict customer churn for MTN network.

Problem Statement

• Identifying customers likely to churn to improve retention strategies.

Data

• Source: Customer data from MTN network.
• Description: The dataset contains customer information and churn labels.
• Features: Customer demographics, usage patterns, churn labels.

Methodology

• Data preprocessing, feature engineering, and model selection including Logistic Regression, Random Forest, and Gradient Boosting.

Results

• Random Forest and Gradient Boosting models showed high accuracy in predicting churn.

Conclusion

• The models can effectively predict churn, aiding in targeted retention efforts.

Code Structure

Directory Structure

mtn/
├── data/
│   ├── mtn_data.csv
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── data_preprocessing.ipynb
│   ├── model_development.ipynb
├── models/
├── requirements.txt
├── readme.md

Code Explanation

• notebooks/data_exploration.ipynb: Exploratory data analysis.
• notebooks/data_preprocessing.ipynb: Data cleaning and preprocessing.
• notebooks/model_development.ipynb: Model training and evaluation.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • scikit-learn: 0.24.1
  • matplotlib: 3.3.4
  • seaborn: 0.11.1

Model Details

Architecture

• Logistic Regression, Random Forest, Gradient Boosting.

Training

• Data split into training and test sets, hyperparameter tuning using RandomizedSearchCV.

Evaluation

• Metrics: Accuracy, Precision, Recall, F1-Score.
• Results: Random Forest and Gradient Boosting achieved high accuracy and F1-Score.

Deployment

Deployment Strategy

• Streamlit app for model deployment.

Infrastructure

• Hosted on a cloud platform with Streamlit.

Future Work

• Implement additional models like XGBoost.
• Enhance feature engineering with more customer behavior data.

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Project 6: [Non Performing Loans Analysis]("/Non Performing Loans")

Project Overview

Goals

• Predict non-performing loans (NPL) for financial institutions.

Problem Statement

• Identifying loans likely to default to mitigate financial risk.

Data

• Source: Loan data from financial institutions.
• Description: The dataset contains loan information and default labels.
• Features: Loan amount, interest rate, borrower information, default labels.

Methodology

• Data preprocessing, feature engineering, and model selection including Logistic Regression, Random Forest, and Gradient Boosting.

Results

• Random Forest and Gradient Boosting models showed high accuracy in predicting NPL.

Conclusion

• The models can effectively predict NPL, aiding in risk management.

Code Structure

Directory Structure

NPL/
├── data/
│   ├── npl_data.csv
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── data_preprocessing.ipynb
│   ├── model_development.ipynb
├── models/
├── requirements.txt
├── readme.md

Code Explanation

• notebooks/data_exploration.ipynb: Exploratory data analysis.
• notebooks/data_preprocessing.ipynb: Data cleaning and preprocessing.
• notebooks/model_development.ipynb: Model training and evaluation.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • scikit-learn: 0.24.1
  • matplotlib: 3.3.4
  • seaborn: 0.11.1

Model Details

Architecture

• Logistic Regression, Random Forest, Gradient Boosting.

Training

• Data split into training and test sets, hyperparameter tuning using RandomizedSearchCV.

Evaluation

• Metrics: Accuracy, Precision, Recall, F1-Score.
• Results: Random Forest and Gradient Boosting achieved high accuracy and F1-Score.

Deployment

Deployment Strategy

• Not applicable for this project.

Infrastructure

• Not applicable for this project.

Future Work

• Implement additional models like XGBoost.
• Enhance feature engineering with more borrower information.

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Project 7: [Music Recommender System]("/Music Recommender System")

Project Overview

Goals

• Develop a recommendation system to suggest items to users based on their preferences and behavior.

Problem Statement

• Providing personalized recommendations to enhance user experience and engagement.

Data

• Source: User interaction data.
• Description: The dataset contains user-item interactions, item metadata, and user profiles.
• Features: User ID, Item ID, Interaction Type, Timestamp.

Methodology

• Data preprocessing, feature engineering, and model selection including collaborative filtering, content-based filtering, and reinforcement learning.

Results

• The recommendation system was able to provide accurate and relevant recommendations.

Conclusion

• The system can be used to improve user engagement and satisfaction by providing personalized recommendations.

Code Structure

Directory Structure

recommender system/
├── data/
│   ├── dataset.csv
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── model_development.ipynb
├── scripts/
│   ├── cbf_recommendations.py
│   ├── combined_recommendations.py
│   ├── final_recommendations.py
│   ├── get_recommendations.py
├── tests/
│   ├── app.py
│   ├── streamlit/
│   │   ├── core_functions.py
│   │   ├── streamlit_interfaces.py
│   ├── gradio/
│   │   ├── core_functions.py
│   │   ├── gradio_interfaces.py
├── readme.md

Code Explanation

• scripts/cbf_recommendations.py: Implements content-based filtering recommendations.
• scripts/combined_recommendations.py: Combines multiple recommendation strategies.
• scripts/final_recommendations.py: Final recommendation logic.
• scripts/get_recommendations.py: Core recommendation functions.
• tests/app.py: Unit tests for recommendation functions.
• tests/streamlit/core_functions.py: Streamlit interface core functions.
• tests/gradio/core_functions.py: Gradio interface core functions.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • scikit-learn: 0.24.1
  • numpy: 1.19.5
  • streamlit: 0.79.0
  • gradio: 2.3.0

Model Details

Architecture

• Collaborative filtering, content-based filtering, reinforcement learning.

Training

• Data split into training and test sets, hyperparameter tuning using GridSearchCV.

Evaluation

• Metrics: Precision, Recall, F1-Score.
• Results: The combined recommendation strategy achieved high precision and recall.

Deployment

Deployment Strategy

• Streamlit and Gradio apps for model deployment.

Infrastructure

• Hosted on a cloud platform with Streamlit and Gradio.

Future Work

• Implement additional models like matrix factorization.
• Enhance feature engineering with more user behavior data.

---

Target Audience: Data scientists.

Style Guide: Use consistent formatting and clear headings for readability.

Code Snippets: Include relevant code snippets to illustrate key points.

Visualizations: Use visualizations to enhance understanding.

Version Control: Use version control for the documentation to track changes and updates.

---

Project 8: Banks

Project Overview

Goals

• Analyze banking data to derive insights and trends.

Problem Statement

• Understanding customer behavior and improving banking services.

Data

• Source: Banking transaction records.
• Description: The dataset contains transaction details, customer demographics, and account information.
• Features: Transaction ID, Customer ID, Transaction Amount, Transaction Type, Timestamp.

Methodology

• Data preprocessing, exploratory data analysis (EDA), and statistical modeling.

Results

• Identified key trends and patterns in customer transactions.

Conclusion

• The analysis provides actionable insights for improving customer service and operational efficiency.

Code Structure

Directory Structure

Banks/
├── analysis.html

Code Explanation

• analysis.html: Contains the results of the data analysis, including visualizations and statistical summaries.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • matplotlib: 3.3.4
  • seaborn: 0.11.1

Analysis Details

Data Preprocessing

• Cleaning and transforming the data for analysis.

Exploratory Data Analysis (EDA)

• Visualizing transaction patterns and customer behavior.

Statistical Modeling

• Applying statistical techniques to identify significant trends.

Future Work

• Implement machine learning models for predictive analysis.
• Enhance data visualization with interactive dashboards.

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Project 9: [Burn out AnalysiS In Medical Imaging Students]("/Burn out AnalysiS In Medical Imaging Students")

Project Overview

Goals

• Assess and analyze burnout levels among students.

Problem Statement

• Understanding the factors contributing to student burnout and its impact on academic performance.

Data

• Source: Survey data from students.
• Description: The dataset contains responses to burnout-related questions.
• Features: Timestamp, Consent, Gender, Age, University, Level, Religion, Location, Ethnicity, Study's financing, Medications intake, Desired profession, Burnout-related questions.

Methodology

• Data preprocessing, feature engineering, and statistical analysis.

Results

• Calculated burnout scores and identified key factors contributing to burnout.

Conclusion

• The analysis provides insights into student burnout and potential interventions.

Code Structure

Directory Structure

Burn out/
├── analysis_v2.ipynb
├── analysis.ipynb
├── analysis.py
├── Burn_Out_Data Set_Adepa.sav
├── Burnout calculated scores
├── Burnout calculated scores.csv
├── burnout_regression_summary.csv
├── Burnout.csv
├── Copy of Burn_Out_Data_Set_Adepa(1)(AutoRecovered).xlsx

Code Explanation

• analysis_v2.ipynb: Contains the updated analysis with refined methodologies.
• analysis.ipynb: Contains the initial analysis and exploratory data analysis (EDA).
• analysis.py: Python script for data preprocessing and analysis.
• Burnout.csv: Raw survey data.
• Burnout calculated scores.csv: Calculated burnout scores.
• burnout_regression_summary.csv: Summary of regression analysis results.

Dependencies

• Python: 3.8
• Libraries:
  • pandas: 1.2.3
  • numpy: 1.19.5
  • matplotlib: 3.3.4
  • seaborn: 0.11.1
  • scikit-learn: 0.24.1

Analysis Details

Data Preprocessing

• Cleaning and transforming the survey data.

Feature Engineering

• Defining burnout score categories: Emotional Exhaustion (EE), Cynicism (CY), and Academic Efficacy (AE).

Statistical Analysis

• Calculating burnout scores and performing regression analysis.

Future Work

• Implement machine learning models to predict burnout.
• Conduct longitudinal studies to track burnout over time.

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