The  Analyst and Consulting from Brownsville. 

Junior or Entry Level Data Analytics

SUMMARY

My name is Iris Angeles, and I am currently enrolled in a CS50 certification program at Harvard University. I recently obtained the Certification of Postgraduate Program in Data Science in Business Analytics from McCombs School of Business at The University of Texas at Austin on October 22, 2023. I aim to join a data science team and leverage my background experience and achievements in Engineering, Business, or Health to contribute to the field. 

In my previous experience, I taught several courses such as Statistics I, Statistics II, Probability and Statistics, and Quality Control. Additionally, I taught students how to analyze data using real-world projects. My role as an Instructor allowed me to analyze data from various fields such as HR, Accounting, Administration, and Industrial Engineering. As a Process, Quality Control, and Field Control Engineer, I applied data analysis techniques to control the process product in various domains such as incoming inspections, warehouse inventories, management data, and oil stations. Through analyzing data and information from these domains, I have gained valuable insights.

Furthermore, I am presenting a catalog of Python projects available on Databricks.

Problem Statement

Business Context

Buying and selling used phones and tablets used to be something that happened on a handful of online marketplace sites. But the used and refurbished device market has grown considerably over the past decade, and a new IDC (International Data Corporation) forecast predicts that the used phone market would be worth $52.7bn by 2023 with a compound annual growth rate (CAGR) of 13.6% from 2018 to 2023. This growth can be attributed to an uptick in demand for used phones and tablets that offer considerable savings compared with new models.

Refurbished and used devices continue to provide cost-effective alternatives to both consumers and businesses that are looking to save money when purchasing one. There are plenty of other benefits associated with the used device market. Used and refurbished devices can be sold with warranties and can also be insured with proof of purchase. Third-party vendors/platforms, such as Verizon, Amazon, etc., provide attractive offers to customers for refurbished devices. Maximizing the longevity of devices through second-hand trade also reduces their environmental impact and helps in recycling and reducing waste. The impact of the COVID-19 outbreak may further boost this segment as consumers cut back on discretionary spending and buy phones and tablets only for immediate needs.

Objective

The rising potential of this comparatively under-the-radar market fuels the need for an ML-based solution to develop a dynamic pricing strategy for used and refurbished devices. ReCell, a startup aiming to tap the potential in this market, has hired you as a data scientist. They want you to analyze the data provided and build a linear regression model to predict the price of a used phone/tablet and identify factors that significantly influence it.

Data Description

The data contains the different attributes of used/refurbished phones and tablets. The data was collected in the year 2021. The detailed data dictionary is given below.

• brand_name: Name of manufacturing brand

• os: OS on which the device runs

• screen_size: Size of the screen in cm

• 4g: Whether 4G is available or not

• 5g: Whether 5G is available or not

• main_camera_mp: Resolution of the rear camera in megapixels

• selfie_camera_mp: Resolution of the front camera in megapixels

• int_memory: Amount of internal memory (ROM) in GB

• ram: Amount of RAM in GB

• battery: Energy capacity of the device battery in mAh

• weight: Weight of the device in grams

• release_year: Year when the device model was released

• days_used: Number of days the used/refurbished device has been used

• normalized_new_price: Normalized price of a new device of the same model in euros

• normalized_used_price: Normalized price of the used/refurbished device in euros

Importing necessary libraries

# Libraries to help with reading and manipulating data

import numpy as np

import pandas as pd

# Libraries to help with data visualization

import matplotlib.pyplot as plt

import seaborn as sns

sns.set()

# split the data into train and test

from sklearn.model_selection import train_test_split

# to build linear regression_model

from sklearn.linear_model import LinearRegression

# to check model performance

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

# to build linear regression_model using statsmodels

import statsmodels.api as sm

# to compute VIF

from statsmodels.stats.outliers_influence import variance_inflation_factor

Loading the dataset

#from google.colab import files

#uploaded=files.upload()

Contact me at 956 465 8153