Project 01: Analyzing the Trends of COVID-19 with Python
Industry: Analytics
Problem Statement: Understanding the trends of COVID-19 spread and checking if restrictions imposed by governments around the world have helped us curb COVID-19 cases and by what degree
Topics: In this project, we will use Data Science and Python and perform visualizations to better understand the data on COVID-19. We will also use time series analysis to make predictions about future cases.
Highlights:
- Using Pandas to accumulate data from multiple data files
- Using plotly to create interactive visualizations
- Using Facebook’s Prophet library to make time series models
- Visualizing the prediction by combining these technologies
Project 02: Customer Churn Classification
Topics: This is a real-world project that gives you hands-on experience in working with most of the ML algorithms.
Highlights:
- Manipulating data to gain meaningful insights
- Visualizing data to figure out trends and patterns among different factors
- Implementing these algorithms: linear regression, decision tree, and Naïve Bayes
Project 03: Creating a Recommendation System for Movies
Topics: This is a real-world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project requires you to deeply understand information filtering, recommender systems, user ‘preference’, and more. You will exclusively work on data related to user details, movie details, and others.
Highlights:
- Recommendation for movies
- Two types of predictions: Rating prediction and item prediction
- Important approaches: Memory-based and model-based
- Knowing user-based methods in k-nearest neighbor
- Understanding the item-based method
- Matrix factorization
- Decomposition of singular value
- Data Science project discussion
- Collaboration filtering
- Business variables overview
Case Study 01: Decision Tree
Topics: Understand the structure of a dataset (PIMA Indians Diabetes database) and create a decision tree model based on it by using Scikit-Learn
Case Study 02: Insurance Cost Prediction (Linear Regression)
Topics: Understand the structure of a medical insurance dataset, implement both simple and multiple linear regressions, and predict values.
Case Study 03: Diabetes Classification (Logistic Regression)
Topics: Understand the structure of a dataset (PIMA Indians Diabetes dataset); implement multiple logistic regressions and classify; fit your model on the test and train data for prediction; evaluate your model using confusion matrix, and then visualize it
Case Study 04: Random Forest
Topics: Create a model that would help in classifying whether a patient ‘is normal,’ ‘is suspected to have a disease,’ or in actuality ‘has the disease’ using the ‘Cardiotocography’ dataset
Case Study 05: Principal Component Analysis (PCA)
Topics: Read the sample Iris dataset; use PCA to figure out the number of most important principal features, and then reduce the number of features using PCA; train and test the random forest classifier algorithm to check if reducing the number of dimensions is causing the model to perform poorly, and figure out the most optimal number that produces good quality results and predicts accurately
Case Study 06: K-means Clustering
Topics: Analyze data; extract useful columns from the dataset; visualize the data; find out the appropriate number of groups or clusters for the data to be segmented (using the elbow method); using k-means clustering, segment the data into k groups (k is found in the previous step); visualize a scatter plot of clusters, and a lot more
