Flower Classification

applying machine learning techniques to classify the images of iris flower

Requirements

python 
numpy 
scipy
pandas 
matplotlib
sklearn

Approach to dealing with machine learning project :

Define Problem.
Prepare Data.
Evaluate Algorithms.
Improve Results.
Present Results.

Step by step instructions

1. Installing the Python and SciPy platform.
2. Loading the dataset.
3. Summarizing the dataset.
4. Visualizing the dataset.
5. Evaluating some algorithms.
6. Making some predictions.

Various Machine Learning Models

Simple Linear Algorithms
    1. Logistic Regresion (LR)
    2. Linear Discriminant Analysis (LDA)
Non Linear Algorithms
    1. K-Nearesrt Neighbors (KNN)
    2. Classification and Regression Trees (CART)
    3. Gaussian Naive Bayes (NB)
    4. Support Vector Machine (SVM)

Example Application

Classification of Iris Flowers

References

ML in python step by step

Dataset