Welcome to Learn_with_Ankith! In this tutorial, we'll cover the crucial data preprocessing steps to ensure your datasets are in top condition before feeding them into your machine learning models. A clean and well-prepared dataset forms the basis for accurate and reliable model predictions.

Data_set link: https://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who

Topics covered:
Data cleaning/preprocessing of data before building a model – a comprehensive guide

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

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

Health check:

Identify and handle missing values effectively.
Explore the shape, information, and spot duplicates of the dataset.
Perform 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.
Discover patterns and relationships with scatterplots and correlation heatmaps.
Missing value treatment:

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

Discover methods for detecting and addressing outliers that can impact model performance.
Data encryption:

Convert categorical variables to a format suitable for machine learning algorithms.
Whether you're a beginner or a seasoned data scientist, mastering these preprocessing techniques is fundamental to building robust and accurate machine learning models. , #OutlierDetection, #MissingValueTreatment, #DataVisualization, #Programming, #DataManipulation, #CodingTips, #FeatureEngineering, #DataQuality, #Pandas, #NumPy, #Matplotlib, #Seaborn, #DataInsights, #TechTutorial, #DataEngineering, #MachineLearningModels, # AIProgramming, #DataAnalytics, #DataWrangling, #TechEducation, #PythonTips, #Statistics, #DataSkills, #ProgrammingLife, #Algorithm, #TechTalk, #CodingCommunity, #DataPrep, #CodeNewbie, #DataQualityCheck, #LearnDataScience, #ProgrammingJourney

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