Project Title - Customer Personality Analysis

The Customer Personality data Analysis is one of the best analysis to collect a information from the customer and in which data maximum information is given. We analysis some informations to get important data like customer in which product to money investing. This data set collecting from kaggle.com, and in this data to analysis with the help of some python program, numpy, pandas, matplotlib, piechart performing in Jupyternotebook. We learn data analysis from Zero to Panda is one of the best platform to learn data analysis course Data Analysis with Python: Zero to Pandas, and we learn how to analysis data with the help of pandas numeric calculation numpy visualization etc.

Description about a data:

This dataset contains 29 variables and 2240 observations about different customers.

Here's a brief version of the data description file.

People

ID: Customer's unique identifier

Year_Birth: Customer's birth year

Education: Customer's education level

Marital_Status: Customer's marital status

Income: Customer's yearly household income

Kidhome: Number of children in customer's household

Teenhome: Number of teenagers in customer's household

Dt_Customer: Date of customer's enrollment with the company

Recency: Number of days since customer's last purchase

Complain: 1 if customer complained in the last 2 years, 0 otherwise

Products

MntWines: Amount spent on wine in last 2 years

MntFruits: Amount spent on fruits in last 2 years

MntMeatProducts: Amount spent on meat in last 2 years

MntFishProducts: Amount spent on fish in last 2 years

MntSweetProducts: Amount spent on sweets in last 2 years

MntGoldProds: Amount spent on gold in last 2 years

Promotion

NumDealsPurchases: Number of purchases made with a discount

AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise

AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise

AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise

AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise

AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise

Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

Place

NumWebPurchases: Number of purchases made through the company’s web site

NumCatalogPurchases: Number of purchases made using a catalogue

NumStorePurchases: Number of purchases made directly in stores

NumWebVisitsMonth: Number of visits to company’s web site in the last month

Let's begin by downloading the data, and listing the files within the dataset.