• performed data cleaning operation
• Outlier and missing value treatments
• Finding no'of clusters using Silhoutte Score
• Built model with both K-means clustering and heirarchical clustering and compared both the results

Python Version: 3.7 Packages: Pandas, numpy, sklearn, matplotlib, seaborn, sklearn, scipy

• Data Quality checks on columns like exports and imports
• Rounding of the numbers to 2 decimal points for ease of analysis

• Checking for missing values
• Outlier identification and capping the upper quartile to 99th percentile
• Univariate and bivariate analysis
  
• burundi has the low income as per primary analysis and can be a country in desire need of aid

• Before starting clustering the data first i have started with hopkins score to see the cluster tendensy
• Acheived result of hopkins score: 0.96
• Scaled data by importing StandardScalar from sklearn

Started building the model with K means clustering and to determine no'of clusters i have used Silhoutte Score

• From the above elbow curve i have concluded to use k = 4 for my model

• Plotted the clusters for GDPP vs Child_mortality and noted the cluters which have high mortality rate as these countries which needs immediate aid
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• differentiated clusters based on child_mortality, income, gdpp
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• Cluster 3 having the list of countries which we need to focus on funding on priority.