BestK.py

def chooseBestKforKMeans(scaled_data, k_range):
    ans = []
    for k in k_range:
        scaled_inertia = kMeansRes(scaled_data, k)
        ans.append((k, scaled_inertia))
    results = pd.DataFrame(ans, columns = ['k','Scaled Inertia']).set_index('k')
    best_k = results.idxmin()[0]
    return best_k, results