Data Analyst Tutorial with Python: Iris Dataset for Beginners 🚀

Learn how to become a Data Analyst by working on the Iris Dataset using Python. This step-by-step tutorial includes data importing, preparation, exploratory data analysis (EDA), and logistic regression model building. Perfect for beginners looking to improve their data science and analytics skills.

In this tutorial, you’ll cover:

Importing and loading datasets

Visualizing data with bar charts, box plots, pair plots, and heatmaps

Splitting data and building predictive models

Applying logistic regression

Evaluating the model with confusion matrix

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00:04 - Import Dataset Iris, pandas read_csv, load iris.csv file
00:20 - Load Dataset in Pandas, view dataframe
00:30 - About Iris Dataset, features and target variable explained
01:35 - Data Preparation, cleaning and formatting data
01:55 - EDA Bar Chart, visualize class distribution
02:29 - EDA Box Plots, detect outliers and variance
03:11 - EDA Pair Plot, seaborn pairplot for feature relationships
03:34 - EDA Correlation Matrix, feature correlation in iris dataset
03:55 - EDA Heatmap Visualization, seaborn heatmap correlation
04:11 - Model Building, preparing data for ML
04:24 - Train the Model, training logistic regression
04:42 - Split the Data, train-test split using sklearn
04:58 - Apply Logistic Regression, sklearn LogisticRegression model
05:12 - Confusion Matrix Evaluation, performance metrics for classification