Label Encoding vs Ordinal Encoding | Categorical Variable Encoding

Machine Learning algorithm cant work on categorical data so we have to encode categorical variables in a numerical format and in this blog we working on Label Encoding and Ordinal Encoding. Below is Jupyter Notebook file to download with practical and prime video tutorial link. What is Ordinal Variables? •Ordinal variables are categorical variables in which the categories can be meaningfully...
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Feature Scaling Standardization and normalization

Feature Scaling – Standardization vs Normalization Explain in Detail

What is Feature Scaling? •Feature Scaling is a method to scale numeric features in the same scale or range (like:-1 to 1,  0 to 1). •This is the last step involved in Data Preprocessing and before ML model training. •It is also called as data normalization. •We apply Feature Scaling on independent variables. •We fit feature scaling with train data and transform on train and test data....
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handling missing values

Best 6 Methods to Handling Missing Values/Data Smartly – Data Cleaning

Data is a powerful weapon to handle the world on the finger. As you know Customer is the king of business but customer's data is the god of the customer and if you have customer's god in your database then you can handle customers according to you. It is the truth. Now let's talk about data cleaning, In this tutorial, we are working on How to handle missing values or data by different methods to...
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Python Seaborn Tutorial

Seaborn Pairplot in Detail| Python Seaborn Tutorial

Seaborn Pairplot uses to get the relation between each and every variable present in Pandas DataFrame. It works like a seaborn scatter plot but it plot only two variables plot and sns paiplot plot the pairwise plot of multiple features/variable in a grid format. How to plot Seaborn Pairplot? To plot seaborn pairplot we use sns.pairplot() function. Syntax: sns.pairplot(       ...
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House Price Prediction - Kaggle Competition - ML Project

ML Project: House Prices Prediction Advanced Regression Techniques | Kaggle Competition

In this project, we are going to predict the price of a house using its 80 features. Basically we are solving the Kaggle Competition. Follow the “House Prices Prediction: Advanced Regression Techniques End to End Project” step by step to get 3 Bonus.1. Raw Dataset2. Ready to use Clean Dataset for ML project 3. Full Project in Jupyter Notebook File house-prices-advanced-regression-techniques...
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ML Project: Breast Cancer Detection Using Machine Learning Classifier

Breast cancer is a dangerous disease for women. If it does not identify in the early-stage then the result will be the death of the patient. It is a common cancer in women worldwide. Worldwide near about 12% of women affected by breast cancer and the number is still increasing. The doctors do not identify each and every breast cancer patient. That's the reason Machine Learning Engineer / Data...
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Directing Customers To Subscription Through App Behaviour Machine Learning Project

ML Project: Directing Customers to Subscription Through Financial App Behavior Analysis

In this Machine Learning end to end project, we are working on financial application data and predict the customer who will take a premium version app subscription or not. Then the company will take action on the customers to give the offers or not. The data contain the customer's behavior and our job to find the insights from it. To complete this project, we use python programming and it's...
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Seaborn Heatmap using sns.heatmap() | Python Seaborn Tutorial

In python seaborn tutorial, we are going to learn about seaborn heatmap or sns heatmap. The sns is short name use for seaborn python library. The heatmap especially uses to show 2D (two dimensional ) data in graphical format. Hey, don't worry. we will talk about step by step in later with practical. I hope, you are following python seaborn, matplotlib, numpy, and pandas tutorials because in...
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Python Seaborn Tutorial

Seaborn Scatter Plot using sns.scatterplot() | Python Seaborn Tutorial

If, you have x and y numeric or one of them a categorical dataset. You want to find the relationship between x and y to getting insights. Then the seaborn scatter plot function sns.scatterplot() will help. Along with sns.scatterplot() function, seaborn have multiple functions like sns.lmplot(), sns.relplot(), sns.pariplot(). But sns.scatterplot() is the best way to create sns scatter plot....
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