[SOLVED] Data-science – eda project

For this project, you’ll work with data from Instacart.
The dataset we’ve provided for you has been modified from the original. We’ve reduced the size of the dataset so that your calculations run faster and we’ve introduced missing and duplicate values. We were also careful to preserve the distributions of the original data when we made our changes.
Your mission is to clean up the data and prepare a report that gives insight into the shopping habits of Instacart customers.
Data dictionary
There are five tables in the dataset, and you’ll need to use all of them to do your data preprocessing and EDA. Below is a data dictionary that lists the columns in each table and describes that data that hold.
instacart_orders.csv: each row corresponds to one order on the Instacart app – order_id: ID number that uniquely identifies each order user_id: ID number that uniquely identifies each customer account – order_number: the number of times this customer has placed an order order_dow: day of the week that the order placed (which day is 0 is uncertain) – order_hour_of_day: hour of the day that the order was placed days_since_prior_order: number of days since this customer placed their previous order
products.csv: each row corresponds to a unique product that customers can buy – product_id: ID number that uniquely identifies each product – product_name: name of the product – aisle_id: ID number that uniquely identifies each grocery aisle category – department_id: ID number that uniquely identifies each grocery department category
order_products.csv: each row corresponds to one item placed in an order – order_id: ID number that uniquely identifies each order product_id: ID number that uniquely identifies each product – add_to_cart_order: the sequential order in which each item was placed in the cart
– reordered: 0 if the customer has never ordered this product before, 1 if they have aisles.csv: – aisle_id: ID number that uniquely identifies each grocery aisle category – aisle: name of the aisle
departments.csv: – department_id: ID number that uniquely identifies each grocery department category – department: name of the department
Stages
Your project will consist of three stages: 1) Open the data files 2) Preprocess the data 3) Perfroma analysis on the data