Motivated by 24 Ultimate Data Science Projects To Boost Your Knowledge and Skills (& can be accessed freely)

• Python 3

Libraries: scikit learn, pandas, seaborn, keras

1. Iris Species

Dataset: 3 Iris species with 50 samples each and 4 properties, i.e., Sepal Length, Sepal Width, Petal Lenth, Petal Width (in cm).

Problem: classify 3 Iris species

Classification techniques: Logistic Regression, Naive Bayes, kNN, SVM, Decision Tree, Boosted Tree, Random Forest, MLP

2. Loan Prediction

Dataset: custumers' details such as Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others.

Problem: predict loan eligibility (Y/N)

Classification techniques: Linear SVC, SVC, kNN, Random Forest

6. Wine Quality

Dataset

Problem:

Classification techniques:

3. Bigmart Sales

Dataset: 2013 sales data for 1559 products across 10 stores in different cities with certain attributes of each product and store such as weight, maximum retail price, size of store and so on.

Problem: predict sales

Regression techniques: Linear Regression, Neural Network

4. Boston House Price

Dataset: 506 cases with 14 attributes for each.

Problem: predict NOX (nitrous oxide level) and MEDV (median value of a home price)

Regression techniques: Linear Regression, ElasticNetCV, LassoCV

5. Time Series

Dataset:

Problem:

8. Height Weight Prediction

Dataset: Height and weight records of 5000 men and 5000 women.

Problem: Predict Weight/Height

Regression techniques: Linear Regression

7. Turkiye Student Evaluation

Dataset: 5820 evaluation scores provided by students from Gazi University in Ankara (Turkey), total of 28 course specific questions and additional 5 attributes.

Problem: Cluster

Clustering techniques: KMeans, MeanShift, BayesianGaussianMixture, AgglomerativeClustering

Every notebook attachs helpful reading-materials. Here are some general ones:

1. Choosing models

• When to Use MLP, CNN, and RNN Neural Networks

• Regularization: Ridge, Lasso and Elastic Net

• 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)

2. Choosing features

• Exploratory Data Analysis
• How to Handle Missing Data
• Visiting: Categorical Features and Encoding in Decision Trees
• Beginner's Guide to Feature Selection in Python
• A Feature Selection Tool for Machine Learning in Python
• Feature Selection For Machine Learning in Python