K-means Clustering Project

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Projects:

Project: To identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.

Objectives:

Exploring Insights/Inferences by performing EDA on the given data. Relevant graphs were plotted to get some insights on data using seaborn package. Model fitting via K-means Clustering by Importing sklearn package.

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Python Libraries Used:

• Pandas
• Numpy
• Matplotlib
• Seaborn
• Scikit learn
• Joblib

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Methodology:

1. Data copying and cleaning:

   • Read the csv file
   • copy the data
   • check for null values and other informations
   • drop the duplicate values

2. Exploratory Data Analysis:

   • Conduct all the necessary EDA using various graphs on the dataset
   • interpret the graphs
   • check for outliers and correlation among the coloumns
   • perform one hot encoding in case of categorical columns

3. Sampling of data:

   • Scaling the data using StandardScaler and Normalizer

4. Modelling of data:

   • import K-means clustering and initialize it
   • determine the k value using elbow method
   • fit the model
   • predict the model

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Contribution

Still Learning,

So feel free, Anything You wanna contirubute.

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