2. Select columns Income and Spending into x U 1 # solution 3. Scale the data using Standard Scaler and output the resul
Posted: Sat May 14, 2022 6:47 pm
Using Python please.
Answer #2-7
Use ANY customer.csv dataset.(I cant provide link due to answers
policy) and (I only need the solution so dataset does not
matter.
2. Select columns Income and Spending into x U 1 # solution 3. Scale the data using Standard Scaler and output the results to scaled_x U 1# solution 4. Run PCA on X and output the results in x_pca U 1 # solution 5. Run silhouette analysis and select the optimum n_clusters U 1 # solution 6. Run KMeans with optimum K and predict the output into cust_km_output U 1 # solution
Answer #2-7
Use ANY customer.csv dataset.(I cant provide link due to answers
policy) and (I only need the solution so dataset does not
matter.
2. Select columns Income and Spending into x U 1 # solution 3. Scale the data using Standard Scaler and output the results to scaled_x U 1# solution 4. Run PCA on X and output the results in x_pca U 1 # solution 5. Run silhouette analysis and select the optimum n_clusters U 1 # solution 6. Run KMeans with optimum K and predict the output into cust_km_output U 1 # solution