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 well-prepared dataset is the foundation for accurate and reliable model predictions.
Data_set link: 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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