Data Science

Data science projects covering data analysis and predictive modeling. Examples of how data can be leveraged for actionable insights.

E-commerce Business Transaction Analysis

Project Overview

This project involves a comprehensive analysis of e-commerce business transactions, utilizing Python to derive insights and make data-driven recommendations. The primary goal is to identify patterns and trends in sales data, understand customer behavior, and suggest actionable strategies to improve business performance.

Objectives

Analyze sales data to identify top-selling products and regions.
Detect trends in customer purchasing behavior.
Identify products with high cancellation rates and provide recommendations to reduce cancellations.
Generate insights to support strategic decisions in marketing and sales.

Methodology

Data Preprocessing: Cleaned and prepared the dataset for analysis by handling missing values, duplicates, and irrelevant data.
Exploratory Data Analysis (EDA): Conducted EDA to understand the distribution of data, identify outliers, and visualize key metrics such as sales trends, top-selling products, and customer demographics.
Data Visualization: Created informative visualizations using libraries such as Matplotlib and Plotly to present findings in an easy-to-understand format.
Statistical Analysis: Performed statistical tests to validate hypotheses and draw meaningful conclusions from the data.
Recommendations: Based on the analysis, provided actionable recommendations to improve sales performance and customer satisfaction.

Key Findings

Identified the top 10 countries with the highest sales, revealing significant market potential outside the UK.
Analyzed customer segments to determine the most profitable customer groups.
Found that two specific products had the highest cancellation rates, leading to significant revenue loss.
Suggested targeted marketing strategies and adjustments in the advertising budget to enhance sales, especially during the holiday season.

Conclusion

The project highlights critical areas of improvement for the e-commerce business and provides data-driven recommendations to optimize sales and marketing strategies. By implementing these suggestions, the business can enhance customer satisfaction, reduce cancellations, and ultimately drive higher revenue growth.

Diabetes Prediction Using Logistic Regression

Project Overview

This project focuses on analyzing a diabetes dataset and building a predictive model using Logistic Regression. The aim is to classify individuals as diabetic or non-diabetic based on various health metrics provided in the dataset.

Objectives

Analyze the diabetes dataset to uncover patterns and insights.
Prepare and clean the data to ensure it is suitable for modeling.
Develop a Logistic Regression model using the scikit-learn library.
Evaluate the model’s performance to understand its accuracy and effectiveness.

Methodology

Data Preprocessing: The dataset was cleaned by handling missing values, normalizing features, and splitting the data into training and testing sets.
Exploratory Data Analysis (EDA): Visualizations and statistical analyses were performed to understand the distribution of features and their relationships with the target variable.
Model Building: A Logistic Regression model was built using the scikit-learn library. The model was trained on the training set and hyperparameters were tuned to optimize performance.
Model Evaluation: The model was evaluated on the testing set, and its performance metrics were calculated.

Results

The Logistic Regression model demonstrated strong performance in predicting diabetes, achieving the following results:

Overall Accuracy: The model correctly predicted whether a person has diabetes in 96% of the cases.
Non-Diabetic Prediction:
- Correctly identified 97 out of 100 people who do not have diabetes.
- Only 3 out of 100 non-diabetic cases were misclassified.
Diabetic Prediction:
- Correctly identified 87 out of 100 people who have diabetes.
- About 13 out of 100 diabetic cases were misclassified as non-diabetic.
Precision: When the model predicts a person is diabetic, it is correct 87% of the time.
Recall: The model identifies 63% of the actual diabetic cases correctly.

These results highlight the model’s strong ability to identify non-diabetic individuals with very high accuracy, while also demonstrating good performance in predicting diabetic cases.

Conclusion

The project successfully created a predictive model that can accurately identify diabetes in individuals based on health metrics. With an overall accuracy of 96%, this model can be a valuable tool in medical diagnosis, helping healthcare providers make informed decisions and potentially saving lives through early detection.

Data Science

Project Overview

Objectives

Analyze sales data to identify top-selling products and regions.

Detect trends in customer purchasing behavior.

Identify products with high cancellation rates and provide recommendations to reduce cancellations.

Generate insights to support strategic decisions in marketing and sales.

Methodology

Data Preprocessing: Cleaned and prepared the dataset for analysis by handling missing values, duplicates, and irrelevant data.

Exploratory Data Analysis (EDA): Conducted EDA to understand the distribution of data, identify outliers, and visualize key metrics such as sales trends, top-selling products, and customer demographics.

Data Visualization: Created informative visualizations using libraries such as Matplotlib and Plotly to present findings in an easy-to-understand format.

Statistical Analysis: Performed statistical tests to validate hypotheses and draw meaningful conclusions from the data.

Recommendations: Based on the analysis, provided actionable recommendations to improve sales performance and customer satisfaction.

Key Findings

Identified the top 10 countries with the highest sales, revealing significant market potential outside the UK.

Analyzed customer segments to determine the most profitable customer groups.

Found that two specific products had the highest cancellation rates, leading to significant revenue loss.

Suggested targeted marketing strategies and adjustments in the advertising budget to enhance sales, especially during the holiday season.

Conclusion

Project Overview

This project focuses on analyzing a diabetes dataset and building a predictive model using Logistic Regression. The aim is to classify individuals as diabetic or non-diabetic based on various health metrics provided in the dataset.

Objectives

Analyze the diabetes dataset to uncover patterns and insights.

Prepare and clean the data to ensure it is suitable for modeling.

Develop a Logistic Regression model using the scikit-learn library.

Evaluate the model’s performance to understand its accuracy and effectiveness.

Methodology

Data Preprocessing: The dataset was cleaned by handling missing values, normalizing features, and splitting the data into training and testing sets.

Exploratory Data Analysis (EDA): Visualizations and statistical analyses were performed to understand the distribution of features and their relationships with the target variable.

Model Building: A Logistic Regression model was built using the scikit-learn library. The model was trained on the training set and hyperparameters were tuned to optimize performance.

Model Evaluation: The model was evaluated on the testing set, and its performance metrics were calculated.

Results

The Logistic Regression model demonstrated strong performance in predicting diabetes, achieving the following results:

Overall Accuracy: The model correctly predicted whether a person has diabetes in 96% of the cases.

Non-Diabetic Prediction:

Correctly identified 97 out of 100 people who do not have diabetes.

Only 3 out of 100 non-diabetic cases were misclassified.

Diabetic Prediction:

Correctly identified 87 out of 100 people who have diabetes.

About 13 out of 100 diabetic cases were misclassified as non-diabetic.

Precision: When the model predicts a person is diabetic, it is correct 87% of the time.

Recall: The model identifies 63% of the actual diabetic cases correctly.

These results highlight the model’s strong ability to identify non-diabetic individuals with very high accuracy, while also demonstrating good performance in predicting diabetic cases.

Conclusion

Tableau Visualizations