This repository contains a jupyter notebook that serves as a template for my data science projects. Feel free to use it for yours as well.

You can find the notebook in this repository as template.ipynb.

• Introduction

  • Description of the project
  • Description of the goals
  • Table of Contents

• Setup

  • Installation of required modules (think about using a requirements.txt file)
  • Importing the necessary modules
  • Setup of various settings that will be used throughout the project. Some examples:
    • Configure the figure_format
    • Set up logging if needed
    • Set seaborn / matplotlib themes
    • Set pandas max columns options

• Data collection

  • Loading the dataset
  • First exploration
    • Head command to see the columns and data
    • Describe command to see the ranges of numerical data
    • Info command as a first quick null check
  • Data Cleaning
    • Transforming data types
    • Handling null values appropriately
    • Merging tables of data to use in EDA

• EDA: Exploratory Data Analysis

  • Univariate exploration
  • Multivariate exploration
  • Correlations

• Statistical Analysis

  • Repeat for every hypothesis:
    • Describe the target populations
    • Describe the null and alternative hypothesis
    • Set the significance level
    • Describe assumptions
    • Describe choice of test
    • Describe the results

• Machine Learning

  • Define one or more prediction goals (repeat next steps for every goal)
    • Load the input data that you need
    • Data preprocessing
      • Address multicollinearity if strong correlations were found during the EDA
      • Think about using dimensionality reduction
      • Label / one-hot encoding
      • Standard scaling
      • Normalization
      • Train - test splitting
    • Model selection and training
      • Explain what model you'll be using
      • Hyperparameter tuning
      • Model training
    • Model evaluation
      • Evaluate model using the metrics of choice.

• Summary

  • Provide an overview of the entire project with key takeaways

• Improvements

  • List the possible improvements that you see