This repository contains INSAID GCDAI curriculum assignments and practice samples.
- The basics of Data Analysis and Data Visualisation with use of
Pandas,Numpy,Matplotlib,Seaborn, etc libraries. - Covers the Exploratory Data Analysis (EDA)
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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 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
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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
- Table of Contents
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CheatSheets contains
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EDA - This repository is about implementation of EDA on the loaded dataset and applying Python fundamentals to it.
- EDA - Summer Olympic Dataset
- EDA - Wine Quality Dataset
- EDA - House Price Prediction
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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 datasetsalong with Model Evaluation
- ML - Fifa 2018 Man of the match Prediction
- ML - Forest Cover Type Prediction
- ML - Iris Dataset Decision Trees and Random Forest
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Python Primer - Basic of Python for Data Science and Machine Learning
