salescsv

# -*- coding: utf-8 -*-
"""ML_P6.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1jcUxUUvU0j7DFv3weJK1HZxEMyspw3pn
"""

import numpy as np
import pandas as pd
import os
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

df = pd.read_csv("./sales_data_sample.csv",encoding='latin')

df.head()

df.describe()

df.shape

df = df[['QUANTITYORDERED', 'ORDERLINENUMBER']]
df = df.dropna(axis = 0)
wcss = []

for i in range(1, 11):
    clustering = KMeans(n_clusters=i, init='k-means++',n_init=10, random_state=42)
    clustering.fit(df)
    wcss.append(clustering.inertia_)

ks = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sns.lineplot(x = ks, y = wcss);

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,5))
sns.scatterplot(ax=axes[0], data=df, x='QUANTITYORDERED', y='ORDERLINENUMBER').set_title('Without clustering')
sns.scatterplot(ax=axes[1], data=df, x='QUANTITYORDERED', y='ORDERLINENUMBER', hue=clustering.labels_).set_title('Using the elbow method');

df.describe().T

from sklearn.preprocessing import StandardScaler

ss = StandardScaler()
scaled = ss.fit_transform(df)
wcss_sc = []

for i in range(1, 11):
    clustering_sc = KMeans(n_clusters=i, init='k-means++',n_init=10, random_state=42)
    clustering_sc.fit(scaled)
    wcss_sc.append(clustering_sc.inertia_)

ks = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sns.lineplot(x = ks, y = wcss_sc);

fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15,5))
sns.scatterplot(ax=axes[0], data=df, x='QUANTITYORDERED', y='ORDERLINENUMBER').set_title('Without cliustering')
sns.scatterplot(ax=axes[1], data=df, x='QUANTITYORDERED', y='ORDERLINENUMBER', hue=clustering.labels_).set_title('With the Elbow method')
sns.scatterplot(ax=axes[2], data=df, x='QUANTITYORDERED', y='ORDERLINENUMBER', hue=clustering_sc.labels_).set_title('With the Elbow method and scaled data');