Welcome to Learn_with_Ankith! In this tutorial, we'll delve into the crucial steps of data preprocessing to ensure your datasets are in prime condition before feeding them into your machine learning models. A clean and wellprepared dataset is the foundation for accurate and reliable model predictions.

Data_set link: https://www.kaggle.com/datasets/kumar...

Topics Covered:
Data Cleaning/Data Preprocessing Before Building a Model A Comprehensive Guide

Import Necessary Libraries: Learn the essential libraries required for efficient data manipulation and analysis.

Read File: Understand how to import data from various sources and formats into your Python environment.

Sanity Check:

Identify and handle missing values effectively.
Explore the dataset's shape, information, and spot duplicates.
Conduct a garbage check to maintain data integrity.
Exploratory Data Analysis (EDA):

Dive into descriptive statistics for a deeper understanding of your data.
Visualize data distributions with histograms and box plots.
Uncover patterns and relationships with scatter plots and correlation heatmaps.
Missing Value Treatment:

Implement strategies using mode, median, and KNNImputer to handle missing data.
Outlier Treatment:

Explore methods to detect and deal with outliers that can impact model performance.
Encoding of Data:

Convert categorical variables into a format suitable for machine learning algorithms.
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