Superstore sales

import calendar import datetime as dt from dateutil import parser from kneed import KneeLocator import numpy as np import matplotlib.pyplot as plt import pandas as pd import plotly import plotly.graph_objects as go from plotly.subplots import make_subplots import plotly.express as px from scipy.stats import zscore import seaborn as sns from sklearn.cluster import KMeans from sklearn.compose import make_column_transformer from sklearn import ensemble from sklearn.ensemble import GradientBoostingRegressor from sklearn.feature_selection import f_regression, SelectKBest from sklearn.linear_model import Lasso,LassoCV, LinearRegression from sklearn.metrics import accuracy_score, mean_absolute_error, mean_squared_error, r2_score from sklearn.model_selection import cross_val_score, GridSearchCV, KFold, train_test_split from sklearn.preprocessing import OneHotEncoder, StandardScaler

df = pd.read_csv("/work/Sample - Superstore.csv")

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for x in df.columns: print(f"{x}, {df[x].unique()}\n")

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df.describe()

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df.isnull().sum()

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df.columns = (df.columns.str.replace(" ", '_', regex=True).str.lower())

df[["order_date","ship_date"]] = df[["order_date","ship_date"]].apply(pd.to_datetime) df["wait_time"] = (df["ship_date"] - df["order_date"]).dt.days

df.wait_time

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df.order_date = pd.to_datetime(df.order_date, format='%M/%d/%Y')

df["day"] = df.order_date.dt.day_name() df["month"] = pd.DatetimeIndex(df.order_date).month df["year"] = pd.DatetimeIndex(df.order_date).year

df["month"] = df["month"].apply(lambda x: calendar.month_abbr[x])

df["customer_id"] = [x.split('-')[1] for x in df["customer_id"]]

df["customer_id"] = df["customer_id"].astype("uint8")

df["profit_per_item"] = df["profit"] / df["quantity"]

revenue = df.sales - df.profit df['gross_price'] = revenue - df.profit / df.quantity

df["net_price"] = df.gross_price - (df.gross_price * df.discount)

df["product_make"] = df["product_name"].str.split(' ').str[0]

df["customer_name"] = df["customer_name"].replace(r'[^0-9a-zA-Z ]', '', regex=True).replace("'", '')

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customer_rating = {0:"platinum", 1:"bronze", 2:"silver", 3:"gold"} clustered_df["customer_rating"] = clustered_df["cluster"].map(customer_rating)

clustered_df.customer_rating.value_counts()

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df = pd.merge(df, clustered_df, on="customer_id", how="outer")

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furniture = furniture.iloc[:, np.r_[0, 11]] office = office.iloc[:, np.r_[0, 11]] tech = tech.iloc[:, np.r_[0, 11]]

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canon_image_class = sales_matrix['Canon imageCLASS 2200 Advanced Copier']

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correlation_with_image_class = correlation_with_image_class.join(sales_data['num_of_sales'])

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y = df.profit

df = df.drop(["order_date", "ship_date", "ship_mode", "customer_name", "wait_time", "postal_code", "city", "sales", "profit_per_item", "gross_price", "profit", "product_name", "product_make", "year"], axis = 1)

X = df

X = pd.get_dummies(df, columns=["segment", "category", "customer_rating", "quantity", "state", "sub-category", "day", "month"])

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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)

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