Mini Projects Inventory

Links to mini projects on data science topics like data wrangling, EDA, ML and data storytelling (Ongoing)

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Table of Contents

1. Data Wrangling

• Data munging on the World Bank projects (working with json) https://github.com/chocolocked/DataWrangling_JsonMiniProject
• User Engagements study using SQL https://modeanalytics.com/chocolocked/reports/04cfdba20eac

2. Exploratory Data Analysis (EDA)

• EDA examining normal range of human body temperature https://github.com/chocolocked/EDA_HumanBodyTemp
• EDA examining the impact of race in resume call-back rate https://github.com/chocolocked/EDA_RacialDiscri
• EDA examining hospital readmission data and making recommendations for reduction https://github.com/chocolocked/EDA_HospitalReadm-

3. Machine Learning (ML)

• Linear Regression on Boston Housing Dataset https://github.com/chocolocked/LinearReg_Boston
• Logistic Regression on Weight vs. Height with Hyperparameter Tuning https://github.com/chocolocked/ML_LogisticReg
• Naive Bayes on predicting Rotten Tomatoes fresh movie reviews with GridSearch on hyperparamter https://github.com/chocolocked/ML_NaiveBayes
• Wine Customer Segmentation using Clustering Methods: Kmeans, Silhouette Method; and Visualizations with PCA https://github.com/chocolocked/ML_Clustering
• RDD operations, Text analysis on Julius Caesar, Linear Regression & Random Forest on Boston Housing Dataset with PySpark Machine Learning libraryhttps://github.com/chocolocked/ML_PySpark

4. Data Challenge

• User adoption analysis, feature engineering, and predictive modeling https://github.com/chocolocked/DataChallenge_relax
• 3 Part ultimate data challenge at https://github.com/chocolocked/DataChallenge_ultimate
• 1. Part 1. Time Series Analysis on user engagement trend;
• 2. Part 2. Experimental design and hypothesis testing;
• 3. Part 3. EDA, feature engineering, pipeline model buidling and recommendations with LIME(Local Interpretable Model-agnostic Explanations)on improving rider retention