pratikbarjatya / insaid-assignment

INSAID - GCDAI

INSAID

This repository contains INSAID GCDAI curriculum assignments and practice samples.

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The repository contains various notebooks that explores

• The basics of Data Analysis and Data Visualisation with use of Pandas, Numpy, Matplotlib, Seaborn, etc libraries.
• Covers the Exploratory Data Analysis (EDA)
  

EDAProject

• What is EDA?

  • EDA is a phenomenon under data analysis used for gaining a better understanding of data aspects like main features of data, variables and relationships that hold between them, identifying which variables are important for our problem
    
  • Exploratory Data Analysis (EDA) helps in understanding the data sets by summarizing their main characteristics often plotting them visually.
  • Lifecycle of a Data Analysis projects consists of:
    

• EDA Methods involve:
  

  • Table of Contents
    Steps in Data Exploration and Preprocessing:
    • Identification of variables and data types
    • Analyzing the basic metrics
      
    • Non-Graphical Univariate Analysis
      
    • Graphical Univariate Analysis
      
    • Bivariate Analysis
      
    • Variable transformations
      
    • Missing value treatment
      
    • Outlier treatment
      
    • Correlation Analysis
      
    • Dimensionality Reduction
      

Repository Overview

• CheatSheets contains

  • Data-Science-Basics-Cheatsheet
  • Pandas-Data-Wrangling-Cheatsheet
  • Python-Machine-Learning-Cheatsheet
  • Seaborn-Cheatsheet

• EDA - This repository is about implementation of EDA on the loaded dataset and applying Python fundamentals to it.

  EDA Project Material:

  • EDA - Summer Olympic Dataset
    • Database
    • Link for the Jupyter notebook
    • Presentation
  • EDA - Wine Quality Dataset
    • Database
    • Link for the Jupyter notebook
  • EDA - House Price Prediction
    • Database
    • Link for the Jupyter notebook

• ML - This repository contains practise samples over various ML Algorithms like Linear Regression, Logistic Regression, Decision Trees, Random Forests, KNN, K-means, etc over openly available datasets along with Model Evaluation

  ML Project Material:

  • ML - Fifa 2018 Man of the match Prediction
    • Link for the Jupyter notebook
  • ML - Forest Cover Type Prediction
    • Link for the Jupyter notebook
  • ML - Iris Dataset Decision Trees and Random Forest
    • Link for the Jupyter notebook

• Python Primer - Basic of Python for Data Science and Machine Learning