All the librarires required to run the code are mentioned in requirements.txt. To install Run: pip install -r requirements.txt
It is very basic classification problem which helps understand basic concept of Machine Learning. The objective of this project was to train a machine learning model to predict class of Iris flower provided with lengths & widths of the flower.
Three class for classification are as follows:
- Iris-setosa
- Iris-versicolor
- Iris-virginica
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Iris.csv: The dataset for this project is downloaded from kaggle.com. The data set consists of 150 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample (in centimetres): Length of the sepals Width of the sepals Length of the petals Width of the petals -
Iris.ipynb: The jupyter notebook Iris.ipynb includes data exploration, code, machine learning model and visualizations. I used the following classification algorithms to train the model and compared their accuracy.- Logistic Regression
- Decision Tree
- Random Forest
- AdaBoost
- XGBoost
I used XGBoost as the final model for this project. The accuracy score was .977
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Iris_Profile_Report.html: Profiling report of the data set -
Visualizations: Includes all plots generated from the training dataCorrelation_Heatmap.png: Correlation heatmap of all featurespairplot.png: Seaborn pairplotSepalWidth_Outliers.png: Outliers detetcted for Sepal Width
Screenshot 1: Correlation Heatmap
Screenshot 2: Outliers in Sepal width
Author: Rahul Gupta Copyright 2020
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