RIDWAN PUADI

Alcoholic Beverages Division (ABD) State of IOWA Customer Segmentation Analysis

Objective

The primary objective of this analysis is to identify opportunities and strategies to increase average monthly sales for Iowa liquor by 2-3% each month. The focus will be on customer segmentation, aiming to understand the distinct preferences and behaviors of different customer groups. By uncovering actionable insights, we aim to optimize marketing efforts, enhance product offerings, and ultimately drive higher sales.

Loan Prediction Problem

Objective

Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.

House Price Prediction

Objective

How to predict house prices in Ames, Iowa, USA by utilizing existing datasets.

Time Series Forecasting

Objective

Unicorn Investors wants to make an investment in a new form of transportation - JetRail. JetRail uses Jet propulsion technology to run rails and move people at a high speed! The investment would only make sense, if they can get more than 1 Million monthly users with in next 18 months. In order to help Unicorn Ventures in their decision, we need to forecast the traffic on JetRail for the next 7 months. We are provided with traffic data of JetRail since inception in the test file.

Machine Learning Pipeline

Introduction

For building any machine learning model, it is important to have a sufficient amount of data to train the model. The data is often collected from various resources and might be available in different formats. Due to this reason, data cleaning and preprocessing becomes a crucial step in the machine learning project. Whenever new data points are added to the existing data, we need to perform the same preprocessing steps again before we can use the model to make predictions. This becomes a tedious and time consuming process!.

Riding Toward Membership: Unveiling Insights from Cyclistic Bike Journey Data for Strategic Marketing Transformation

Objective

Analisis data untuk mengidentifikasi perbedaan utama dalam perilaku, pola penggunaan, dan demografi antara anggota tahunan dan pengendara biasa Cyclistic.
Investigate the reasons why riders would be willing to purchase annual memberships, exploring factors such as cost savings, convenience, or additional benefits.

Enhancing Bellabeat Product Strategy: A Comprehensive Analysis of Non-Bellabeat Smart Device Usage Patterns and Consumer Insights

Objective

Identify and understand patterns of non-Bellabeat smart device usage, including frequently used features, usage times, and user preferences.
Determine consumer preferences regarding non-Bellabeat smart devices, focusing on features or functions considered important by users.

Fintech Case Study: Predictive Modeling and Targeted Strategies for Fintech Free Trial Users

Objective

Create a robust predictive model utilizing user history data during the free trial period to identify patterns and factors that indicate whether a user is likely to convert to a premium subscription.

Dashboard Sales Performance: B2b E-Commerce

Objective

Provide a comprehensive view of total sales, including trends over time.
Evaluate the sales performance of each category to identify high-performing and underperforming areas
Enable users to drill down into specific product details and sales patterns.

Sprocket Central Pty Ltd: Exploring New Customer Dynamics

Objective

Examine the age distribution of new customers to identify the demographic trends and age groups that are most attracted to the product or service.
Analyze the data to understand the frequency and preferences of bike purchases among new customers, identifying popular models or features that contribute to purchase decisions.

CERTIFICATE

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