The speaker is conducting an exploratory data analysis of the IRS project and its dataset, specifically focusing on finding the optimum number of clusters in the IRS Iris dataset. The process begins with importing essential libraries such as pandas, numpy, matplotlib, seaborn, and sklearn.
The data is read from the iris.csv file and a dataframe is created for the dataset. The dataframe is then displayed, showing six columns and 150 rows. The speaker checks for missing values, duplicated columns, and any null values in the columns.
After ensuring there are no missing or null values, the speaker proceeds with univariate analysis of each column in the dataset. This involves checking for value counts, range of values, maximum and minimum values, mean, median, standard deviation, quartile values, interquartile range, and outliers.
Box plots and histograms are created for each variable, followed by multivariate analysis using a pairplot. The correlation between each variable is checked and visualized using a heatmap.
The data is then scaled using the StandardScaler from sklearn. After scaling, the data is again plotted to ensure it is properly distributed.
Hierarchical clustering is applied to the data, and the dendrogram is truncated to a specific number of clusters. The speaker concludes that there are three types of clusters in the data: type 1, type 2, and type 3, with frequencies of 53 and 97 respectively. The speaker invites viewers to ask any questions they may have about the process.
1. The video is about the exploratory data analysis of the IRS project and IRS data set.
2. The main goal is to find the optimum number of clusters present in the IRS Iris data set.
3. The first step is to import important libraries such as pandas, numpy, matplotlib, sklearn, and SQL.
4. The data is read from the CSP file, specifically iris.csv.
5. The data frame for this file is created.
6. The data set contains six columns and 150 rows