Welcome to the world of predictive analytics! Below is a simple guide to building a predictive model using Python. Let's dive into the code:
1. Install Required Libraries 📦
pip install pandas scikit-learn matplotlib
2. Load and Explore Data 📈
import pandas as pd
data = pd.read_csv("your_dataset.csv")
print(data.head())
# 📌 Insert image: Data_Exploration
<center><img src="https://cloud-image.ullrai.com/q/Data_Exploration/" alt="Data Exploration"/></center>
3. Split Data and Train Model 🧱
from sklearn.model_selection import train_test_split
X = data.drop("target", axis=1)
y = data["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# 📌 Insert image: Model_Training
<center><img src="https://cloud-image.ullrai.com/q/Model_Training/" alt="Model Training"/></center>
4. Evaluate Your Model 🧪
from sklearn.metrics import mean_squared_error
model.fit(X_train, y_train)
predictions = model.predict(X_test)
mse = mean_squared_error(y_test, predictions)
print(f"Mean Squared Error: {mse}")
# 📌 Insert image: Model_Evaluation
<center><img src="https://cloud-image.ullrai.com/q/Model_Evaluation/" alt="Model Evaluation"/></center>
5. Deploy for Real-World Use 🚀
- Save model:
joblib.dump(model, "model.pkl") - Load model:
model = joblib.load("model.pkl") - Use in production: Learn more about deployment
📌 Need help with data visualization? Check out our Matplotlib tutorial for advanced plotting techniques!