Build Your First ML Model

Let's build our first Machine Learning model!
We'll predict student marks based on how many hours they studied. 🎯

We’ll use:

Jupyter lab or Google Colab for coding
pandas to handle data
numpy for math
matplotlib to plot
scikit-learn to build the model

Step 1: Import Libraries on Jupyter Lab

python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

Step 2: Create a Dataset

python

data = {
    "Hours": [1, 2, 3, 4, 5, 6, 7],
    "Marks": [10, 20, 30, 40, 50, 60, 70]
}
df = pd.DataFrame(data)
print(df)

Step 3: Visualize the Data

python


plt.scatter(df["Hours"], df["Marks"])
plt.title("Study Hours vs Marks")
plt.xlabel("Hours")
plt.ylabel("Marks")
plt.show()

Step 4: Train the Model

python


X = df[["Hours"]]  # Features
y = df["Marks"]    # Target
model = LinearRegression()
model.fit(X, y)

Step 5: Make Predictions

python


predicted = model.predict([[5]])
print(f"Predicted Marks for 5 hours of study: {predicted[0]:.2f}")

Step 6: Plot the Line

python


plt.scatter(X, y)
plt.plot(X, model.predict(X), color='red')  # Best fit line
plt.title("Study Hours vs Marks (Model)")
plt.xlabel("Hours")
plt.ylabel("Marks")
plt.show()

What You Just Did ✨

Built your first Supervised Learning model
Understood the use of pandas, numpy, matplotlib, and scikit-learn.
Learned how ML predicts future data

Let's build our first Machine Learning model!
We'll predict student marks based on how many hours they studied. 🎯

We’ll use:

Jupyter lab or Google Colab for coding
pandas to handle data
numpy for math
matplotlib to plot
scikit-learn to build the model

Step 1: Import Libraries on Jupyter Lab

python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression

Step 2: Create a Dataset

python

data = {
    "Hours": [1, 2, 3, 4, 5, 6, 7],
    "Marks": [10, 20, 30, 40, 50, 60, 70]
}
df = pd.DataFrame(data)
print(df)

Step 3: Visualize the Data

python


plt.scatter(df["Hours"], df["Marks"])
plt.title("Study Hours vs Marks")
plt.xlabel("Hours")
plt.ylabel("Marks")
plt.show()

Step 4: Train the Model

python


X = df[["Hours"]]  # Features
y = df["Marks"]    # Target
model = LinearRegression()
model.fit(X, y)

Step 5: Make Predictions

python


predicted = model.predict([[5]])
print(f"Predicted Marks for 5 hours of study: {predicted[0]:.2f}")

Step 6: Plot the Line

python


plt.scatter(X, y)
plt.plot(X, model.predict(X), color='red')  # Best fit line
plt.title("Study Hours vs Marks (Model)")
plt.xlabel("Hours")
plt.ylabel("Marks")
plt.show()

What You Just Did ✨

Built your first Supervised Learning model
Understood the use of pandas, numpy, matplotlib, and scikit-learn.
Learned how ML predicts future data

Build Your First ML Model

Step 1: Import Libraries on Jupyter Lab

Step 2: Create a Dataset

Step 3: Visualize the Data

Step 4: Train the Model

Step 5: Make Predictions

Step 6: Plot the Line

What You Just Did ✨

AI CodeBuddy

Build Your First ML Model

Step 1: Import Libraries on Jupyter Lab

Step 2: Create a Dataset

Step 3: Visualize the Data

Step 4: Train the Model

Step 5: Make Predictions

Step 6: Plot the Line

What You Just Did ✨