Established supervised machine learning multi-classification algorithm to solve Iris Flower dataset problem using Python. Libraries used: scipy, numpy, matplotib, pandas, sklearn
A machine learning project steps:
- Define Problem.
- Prepare Data.
- Evaluate Algorithms.
- Improve Results.
- Present Results.
The overview of what we are going to cover:
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Installing the Python and SciPy platform. Get the Python and SciPy platform installed on your system if it is not already.
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Loading the dataset. We are going to use the iris flowers dataset.The dataset contains 150 observations of iris flowers. There are four columns of measurements of the flowers in centimeters. The fifth column is the species of the flower observed. All observed flowers belong to one of three species.
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Summarizing the dataset. In this step we are going to take a look at the data a few different ways: Dimensions of the dataset. Peek at the data itself. Statistical summary of all attributes. Breakdown of the data by the class variable.
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Visualizing the dataset. We are going to look at two types of plots: - Univariate plots to better understand each attribute. - Multivariate plots to better understand the relationships between attributes.
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Evaluating some algorithms. Here is what we are going to cover in this step: - Separate out a validation dataset. - Set-up the test harness to use 10-fold cross validation. - Build 5 different models to predict species from flower measurements - Select the best model.
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Making some predictions.