Data Science Projects

Python Programming for Data Science

  1. Python Exercises
  2. List Comprehensions and Pandas Exercises
  3. Lead Calculation with Rule-Based Classification
  4. Advanced Exploratory Data Analysis and Data Visualization using Netflix Dataset

Customer Relationship Management (CRM) Analysis

  1. Recency (R), Frequency (F) and Monetary (M) Segmentation of Customers using Online Retail Dataset
  2. Customer Lifetime Value (CLTV) Calculation using Online Retail Dataset
  3. Customer Lifetime Value (CLTV) Prediction with BG-NBD and Gamma Gamma Models using Online Retail Dataset
  4. Customer Lifetime Value (CLTV) Prediction with the models BG-NBD and Gamma-Gamma – FLO Example
  5. Customer Segmentation using RFM and KMeans Methods and Association Rule Analysis – Online Retail Example
  6. Customer Segmentation with RFM and KMeans – FLO Example

Measurement Problems

  1. Scoring and sorting the IMDB Top250 Lists
  2. Comparing Conversion of Bidding Methods with AB Testing
  3. Rating products and sorting reviews in amazon
  4. Sorting reviews using the data collected from an e-commerce company
  5. An application for calculating the product rating over the ratings given to a product

Recommendation Systems

  1. Developing an ARL Based Recommender System using the Dataset Online Retail
  2. Developing a Hybrid Recommender System
  3. Developing a Prediction-Based Recommendation System using Model-Based Matrix Factorization
  4. Developing a User-Based Recommendation System using the Datasets ‘Movie and Rating’
  5. Item-Based Recommendation System using the Datasets ‘Movie and Rating’
  6. ARL Recommender System using the Armut Data
  7. Association Rule Learning using the Dataset Online Retail II
  8. Developing a Content-Based Recommendation System using Metadata of Movies

Feature Engineering and Machine Learning

  1. House Price Prediction using PyCaret
  2. Classification of Talent Hunting using PyCaret
  3. Classification of Talent Hunting with Artificial Learning
  4. Customer Segmentation with Unsupervised Machine Learning Models
  5. Developing Machine Learning Models to Predict House Price
  6. Exploratory Data Analysis, Feature Engineering, and Developing a Machine Learning Model to Prevent Telco Customer Churn
  7. Error Evaluation for Regression Models
  8. Estimating CO2 Emissions using Linear Regression and Lazy Regression
  9. Prediction of CO2 Emissions using Linear Regression, Polynomial Regression, KNN, and Lazy Regressor
  10. Analysis of China Gross Domestic Product (GDP)
  11. Hierarchical Clustering Analysis of US Arrests
  12. Diabetes Prediction using Basic Modeling

Natural Language Processing (NLP)

  1. Text Classification and Sentiment Analysis using Natural Language Processing (NLP)

Competitions

  1. Estimation of (Non)-Survival in the Titanic Dataset using Machine Learning Models

Data Analysis

  1. Investigating Netflix Movies and Guest Stars
  2. What and Where are the World’s Oldest Businesses
  3. The Android App Market on Google Play
  4. A Visual History of Nobel Prize Winners
  5. The GitHub History of the Scala Language