There are three ML projects in this repositry. (i) Bank customer churn prediction (ii) House price prediction (iii)Email spam Detection So every project I want to divide it into three parts WHY,HOW AND WHAT
Lets come to the first project i.e, Bank customer churn prediction WHY-Banking is one of those traditional industries that has gone through a steady transformation over the decades.Yet, many banks today with a sizeable customer base hoping to gain a competitive edge have not tapped into the vast amounts of data they have especially in solving one of the most acknowledged problems – customer churn. While retaining existing customers and thereby increasing their lifetime value is something everyone acknowledges as being important, there is little the banks can do about customer churn when they don’t see it coming in the first place. This is where predicting churn at the right time becomes important, especially when clear customer feedback is absent. Early and accurate churn prediction empowers CRM and customer experience teams to be creative and proactive in their engagement with the customer.
HOW-I applied Cross-industry standard process for data mining (CRISP-DM) methodology.https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
WHAT-The aim of bank customer churn prediction is to identify and predict customers who are likely to leave or discontinue their relationship with a bank in the near future.
Churn refers to the phenomenon where customers stop using a company's products or services, and it is a crucial concern for businesses across various industries, including
the banking sector.In the context of banks, customer churn prediction aims to help financial institutions retain their customers by taking proactive measures to prevent
them from leaving.
The primary goals of bank customer churn prediction are:
Retention: By identifying customers who are at a high risk of churning, banks can take targeted actions to retain them.
These actions might include offering special promotions, discounts, personalized services, or improved customer support.
Cost Savings: Acquiring new customers is typically more expensive than retaining existing ones.
By predicting churn and taking steps to prevent it, banks can save on marketing and acquisition costs.
Customer Satisfaction: Addressing the concerns of potential churners can improve overall customer satisfaction.
By demonstrating that the bank cares about their needs and concerns, customers are more likely to remain loyal.
Data-Driven Decision Making: Churn prediction relies on analyzing customer data to identify patterns and trends associated
This process encourages banks to adopt data-driven decision-making approaches, leading to more effective strategies and a better understanding of customer behavior.
Business Performance: Reducing churn can have a positive impact on a bank's bottom line.
Retaining valuable customers contributes to stable revenue streams and supports the bank's long-term financial performance.
To achieve accurate churn prediction, banks typically employ advanced analytics techniques, including machine learning and
They analyze historical customer data, transaction patterns, demographics, customer interactions, and other relevant factors to build predictive models. These models can then
score current customers and identify those at the highest risk of churning.
Lets come to second project i.e, House price prediction
WHY-House price forecasting is an important topic of real estate. The literature attempts to derive useful knowledge from historical data of property markets. Machine learning techniques are applied to analyze historical property transactions in India to discover useful models for house buyers and sellers.
HOW-I applied Cross-industry standard process for data mining (CRISP-DM) methodology.https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining
WHAT-The aim of house price prediction is to estimate the value of residential properties based on various factors and features. This prediction is typically carried out
using statistical and machine learning techniques to analyze historical data and identify patterns that influence property prices.
The primary goals of house price prediction include:
Informed Buying and Selling: For homebuyers and sellers, accurate price predictions provide valuable information for making
informed decisions. Buyers can assess whether a property is fairly priced, and sellers can set a competitive listing price.
Real Estate Investment: Investors use house price predictions to evaluate potential investment opportunities. Predictions help
them identify properties that are likely to appreciate in value over time, which is essential for making profitable investment decisions.
Market Understanding: House price predictions provide insights into local real estate market trends. This information is val
for real estate agents, developers, and policymakers who need to understand market dynamics and make strategic decisions.
Risk Management: Lenders and financial institutions use house price predictions to assess the risk associated with mortgage loa
Accurate predictions help them determine appropriate lending terms and avoid overexposure to properties that might decline in value.
Economic Indicators: Real estate prices can serve as indicators of broader economic trends. Changes in housing prices can ref
shifts in supply and demand, interest rates, and consumer confidence.
Data-Driven Insights: Predicting house prices involves analyzing a wide range of data, including property features (size,
location amenities), market trends, economic indicators, and more. This encourages data-driven decision-making and better understanding of factors affecting property values.
Valuation Services: Real estate professionals and appraisers can use predictive models as a supplement to traditional appraisal
appraisal methods, helping to improve the accuracy of property valuations.
House price prediction is a complex task due to the numerous variables that influence property values. These variables can incl
inculde location, property size, number of bedrooms and bathrooms, proximity to amenities, market trends, economic indicators, and more. Machine learning algorithms are commonly
used to build predictive models that learn from historical data and generalize patterns to predict future prices.
Lets come to third project i.e, Email spam detection ML
WHY-Unwanted communication, or email spam, is a common challenge for many businesses today. Cyberattackers often send mass emails to millions of email addresses they scraped
from the internet. These emails usually have a sense of urgency, lucrative offers, or even mimic other genuine sites. However, the main motivation behind these emails is far
more sinister. Specifically, cyberattackers want people to click on malicious links within these emails. This could result in data theft or even malware downloading itself
onto the victim’s computer. Research shows cybercriminals earn, on average, 7,000 USD from around 14.5 billion spam emails they send out daily. Also, did you know that 46% of
emails in your inbox are spam? Sounds crazy, right? That’s a lot of potentially-dangerous emails! This is why every company that uses emails in corporate communication needs
an email spam filter.
HOW-I applied Cross-industry standard process for data mining (CRISP-DM) methodology.https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining.Along with
that I used some NLP techniques such as TF-IDF which convert text data into numerical data that Mechine learning algorithm can easly understand.
WHAT-The aim of an email detection machine learning (ML) algorithm is to automatically classify incoming emails into predefined categories or labels based on their content,
characteristics, or other features.
Email detection algorithms are commonly used to address different tasks, each with its own specific goal:
Spam Detection: The primary aim of many email detection algorithms is to identify and filter out spam emails, which are
unsolicited and often contain irrelevant or potentially harmful content. By accurately detecting spam, these algorithms help users keep their inboxes clean and avoid phishing
scams, malware, and other security threats.
Ham Classification: "Ham" refers to legitimate, non-spam emails. Algorithms aim to accurately classify incoming emails as eith
either spam or ham. Correctly identifying legitimate emails ensures that important messages are not mistakenly marked as spam and are delivered to the user's inbox.
The aim of an email detection ML algorithm is to enhance the email experience for users by effectively managing and categor
categorizing incoming emails, reducing spam, ensuring the delivery of important messages, and improving overall email security.