Picture this: a busy housing project wanting to connect with people looking to buy homes. They have lots of old information about people's ages, salaries, and whether they've bought a house before. Now, they're on a mission to change how homes are advertised and sold. 💼📊
Here's where machine learning comes in. It's like a superhero! It can understand patterns and guess what might happen next. The housing project teaches the machine using all their old data. This helps them see things they couldn't before. 🤖💡
But how does it work? It's pretty simple. The machine looks at what happened before to figure out who might buy a house next. It looks at things like how old someone is and how much money they make. 📈💭
Now, imagine you are one of sales executive of housing project . You are getting enquiries about recently launched housing project but don't know which audience to target from so many enquiries. That's where machine learning helps out. It looks all the information which you are collecting as a part of enquiry and give you probable prediction if that person is really going to buy home or not . 🎉🔍
So, machine learning isn't just a tool. It's a game-changer!
Let's understand the most excited part :-
Imagine you have a special list with three things: Age, Salary, and Purchased. This list helps us figure out who bought a house and who didn't. If someone bought a house, we write down 1 next to their name. If not, we write 0.
So, with this list, we're like detectives 🕵️ trying to predict who's going to buy a house next! We'll use a cool trick called logistic regression to guess based on people's ages and salaries.
It's like solving a puzzle 🧩 where we use math to find clues and make smart guesses. And who knows? Maybe we'll uncover some surprising secrets about what makes people decide to buy a home 🏡.
Ready to crack the code and unlock the mysteries of home-buying? Let's dive in and see what we can discover together! 🚀✨
🔗 To download and try this code yourself, please click on the Housing_Project_Target_Audience_Project
Below are the step by step how our actual code works and at the end what information we derived from it :-
Getting Ready: First, we import the necessary tools for our adventure. We'll need numpy for number crunching, matplotlib for visualizations, and pandas for handling data. 🛠️
Data Exploration: We load up our dataset, which contains information about potential customers from a previous housing campaign. This includes their age, estimated salary, and whether they responded positively or negatively. 📊
Preparing the Data: Before we can unleash our logistic regression magic, we need to scale our features using StandardScaler. This ensures that each feature contributes equally to our predictions, preventing any biases. 📏
Training the Model: With our data prepped, we train our logistic regression model. This smart algorithm learns from the past responses to predict whether new customers will be interested based on their age and estimated salary. 🧠
Visualizing the Training Results: Time to see how well our model performs! Using matplotlib, we plot the ages and estimated salaries of our previous customers. By coloring them based on their response, we can see how our model draws the line between potential buyers and non-buyers. 📈
Bringing in the Newbies: Now comes the exciting part! We have a new batch of potential customers, and we're eager to see how they fare. We load up their data and get ready to make some predictions. 💼
Visualizing Predictions: As the new customers join the party, we put our model to the test. Using our trained logistic regression model, we predict whether each new customer will be interested in the housing campaign based on their age and estimated salary. Then, we plot them on the same graph, distinguishing them with different colors. 🎉
In conclusion, the black dots scattered within the gray-colored area represent our target audience, those most likely to purchase a home.
And there you have it! With a little bit of coding magic, we've successfully predicted which customers are likely to jump on board with our housing campaign.
This glimpse into the power of machine learning highlights its ability to provide valuable insights and drive impactful outcomes. Stay tuned for more insights into how machine learning can revolutionize problem-solving and unlock endless possibilities!