My First Machine Learning Model: Predicting Whether I Will Like a Movie

As part of my AI/ML Upgrade Phase, I built my first Machine Learning project using Python and Scikit-learn.

The goal was simple:

Can a machine predict whether I will like a movie based on its characteristics?

Although this is a small project, it helped me understand the complete Machine Learning workflow—from creating a dataset to training a model, saving it, and making predictions.

What Is Machine Learning?

In traditional programming, developers write rules manually.

If marks > 50
    Pass
Else
    Fail

Machine Learning works differently.

Instead of writing rules, we provide examples. The algorithm studies those examples, learns patterns, and predicts results for new data.

Step 1: Creating the Dataset

Every ML project begins with data.

length,action,comedy,liked
90,8,2,1
120,9,1,1
80,2,9,1
150,10,1,0
70,1,8,1
140,9,2,0
100,5,5,1
130,8,3,0
95,4,7,1
160,10,1,0

Column Description

length → Movie duration (minutes)
action → Action score (1–10)
comedy → Comedy score (1–10)
liked → Target value

1 = Liked the movie
0 = Did not like the movie

Step 2: Installing Required Libraries

pip install pandas scikit-learn joblib

Pandas

Reads and manages datasets.

Scikit-learn

Provides Machine Learning algorithms.

Joblib

Saves and loads trained models.

Step 3: Loading the Dataset

import pandas as pd

data = pd.read_csv("movies.csv")

Pandas converts the CSV file into a table that Python can understand.

Step 4: Features and Target

Features (Inputs)

X = data[['length','action','comedy']]

Movie Length
Action Level
Comedy Level

Target (Output)

y = data['liked']

The model learns:

Length + Action + Comedy
            ↓
      Like / Don't Like

Step 5: Choosing the Algorithm

For this project, I selected a Decision Tree Classifier.

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()

A Decision Tree learns by asking questions.

Action > 7?
      │
     Yes
      │
Length < 130?
      │
     Yes
      │
    Like

Step 6: Training the Model

model.fit(X, y)

The training process:

Reads every example
Finds patterns
Creates decision rules
Stores learned knowledge

This is where the model becomes "intelligent."

Step 7: Saving the Model

import joblib

joblib.dump(model,"movie_model.pkl")

The file movie_model.pkl contains everything the model learned during training.

Complete Training Script

import pandas as pd
from sklearn.tree import DecisionTreeClassifier
import joblib

data = pd.read_csv("movies.csv")

X = data[['length','action','comedy']]
y = data['liked']

model = DecisionTreeClassifier()

model.fit(X,y)

joblib.dump(model,"movie_model.pkl")

print("Model trained and saved!")

Step 8: Loading the Saved Model

import joblib

model = joblib.load("movie_model.pkl")

No retraining is required. The model is instantly ready for predictions.

Step 9: Making Predictions

length = int(input("Movie Length: "))
action = int(input("Action Level (1-10): "))
comedy = int(input("Comedy Level (1-10): "))

prediction = model.predict([[length,action,comedy]])

if prediction[0]==1:
    print("You will probably LIKE this movie 🎬")
else:
    print("You will probably NOT LIKE this movie ❌")

Example Prediction

Movie Length: 100
Action Level: 7
Comedy Level: 4

Output:

You will probably LIKE this movie 🎬

Conclusion

Building this movie prediction model was my first step into the world of Machine Learning, and it gave me hands-on experience with the complete ML workflow—from creating a dataset and training a model to saving it and making predictions. While the project is simple, it helped me understand the core concepts that power modern AI applications. This experience has motivated me to continue learning advanced topics such as data preprocessing, model evaluation, deep learning, and neural networks. Every expert starts with a beginner project, and this marks the beginning of my AI/ML journey.

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How I Built My First Machine Learning Model Using Python: A Beginner's Guide