https://github.com/rudra00434/logisticregression_model_iris-dataset_hypertuning
Trained a Logistic regression model on iris dataset (hypertuning wth GridSearch_CV )
https://github.com/rudra00434/logisticregression_model_iris-dataset_hypertuning
classification-algorithm iris-dataset logistic-regression machine-learning machine-learning-algorithms sklearn supervised-learning
Last synced: about 1 month ago
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Trained a Logistic regression model on iris dataset (hypertuning wth GridSearch_CV )
- Host: GitHub
- URL: https://github.com/rudra00434/logisticregression_model_iris-dataset_hypertuning
- Owner: rudra00434
- Created: 2025-08-23T15:11:07.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-08-23T16:50:06.000Z (3 months ago)
- Last Synced: 2025-08-24T05:44:37.592Z (3 months ago)
- Topics: classification-algorithm, iris-dataset, logistic-regression, machine-learning, machine-learning-algorithms, sklearn, supervised-learning
- Language: Python
- Homepage:
- Size: 8.79 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# LogisticRegression_Model_Iris-dataset_HyperTuning
Trained a Logistic regression model on iris dataset
## Logistic Regression with Hyperparameter Tuning on Iris Dataset
## Overview
A binary classification project using Logistic Regression to distinguish *versicolor* vs *virginica* Iris species, with **Exploratory Data Analysis** and **GridSearchCV hyperparameter tuning**.
## Key Steps
- Data cleaning & filtering
- EDA: Pairplot, correlation heatmap, histograms, boxplots, violin plots
- Logistic Regression with **GridSearchCV** (5-fold CV) tuning:
- `penalty`, `C`, `solver`, `max_iter`
- Model evaluation: test accuracy, classification report
## Results
- **Best Hyperparameters**: *(fill with your best_params_ output)*
- **Test Accuracy**: *(fill in score)*
- **Classification Report**:
# Visuals of EDA and Histograms and Correlation Heatmaps-violin plots
