Data Science Tools

Exploratory Data Analysis

Analyzing Numerical & Categorical data
Count plots of Categorical variables
Histogram plots of Numerical variables
Box plots of numerical variables based on Categorical variable
Histogram of numerical variables based on Categorical variable
Transformation of Numerical variables
Normalization or Standardization
We can impute the missing values of numerical variables by its median. For categorical variables, missing values are imputed by mode.
We can also try several group be method to decide how to impute.
Handling of outliers
dummy variable creation for categorical variables (escaping multicollinearity)

EDA is extensively applied and some Scikit-Learn based model such as Logistic Regression, Support Vector Machine, K-Nearest Neighbour, Random Forest is used.

MIT

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data science tools		data science tools
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Data Cleaning & Exploratory Data Analysis-202101.ipynb		Data Cleaning & Exploratory Data Analysis-202101.ipynb
README.md		README.md
titanic_data_model (Logistic Regression, SVM, KNN, Random Forest).ipynb		titanic_data_model (Logistic Regression, SVM, KNN, Random Forest).ipynb