What's included

• 1. Data Preprocessing Report

  Cleaned and structured dataset: Comprehensive preprocessing of the raw data into a format suitable for analysis. Summary of preprocessing steps: Detailed explanation of how the data was cleaned, normalized, and structured.

• 2. Exploratory Data Analysis (EDA)

  Visualizations: Charts and graphs illustrating key patterns, trends, and distributions in the data. Descriptive statistics: Insights into the dataset, such as averages, variances, and correlations.

• 3. Clustering Analysis

  Cluster assignment report: Description of distinct groups (clusters) identified in the data. Cluster visualization: Visual representation of clusters for easy interpretation. Optimal model parameters: A detailed report of the K-means model parameters (e.g., distance metric, K value) used for clustering.

• 4. Classification Model

  Trained classifier: The final predictive model (e.g., Logistic Regression, etc.) with optimized parameters. Model performance metrics: Detailed metrics such as accuracy, F1 score, precision, and recall. Confusion matrix: A graphical representation of classification results. Classification report: Detailed analysis of model performance across different classes.

• 5. Predictive Insights & Future Recommendations

  Prediction capabilities: A model capable of predicting new data behavior based on historical trends. Recommendations for future use: Best practices for utilizing the model in future data streams and decision-making.

• 6. Source Code and Documentation

  Well-commented code: Fully documented Python scripts for all preprocessing, clustering, and classification tasks. User guide: Step-by-step instructions for using and adapting the code in future applications.

• 7. Presentation of Findings

  Executive summary: A concise, non-technical overview of the key findings and business implications. Slide deck: A professional presentation summarizing the project's objectives, process, and results.