🤖 AI Lab Repository (Labs 1–12)

This repository contains implementations and examples of core Artificial Intelligence and Data Science concepts, including optimization, probability, machine learning, preprocessing, and visualization.

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📌 Topics Covered

• Problem Modeling & Solving using OR-Tools
• Basic Probability Concepts
• Game Trees, Minimax & Alpha-Beta Pruning
• Exploratory Data Analysis (EDA)
• Data Cleaning & Preprocessing
• Evaluation Metrics
• Machine Learning Algorithms
• Data Visualization

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🧠 1. Problem Modeling & Solving (OR-Tools)

Example: Linear Optimization

from ortools.linear_solver import pywraplp

solver = pywraplp.Solver.CreateSolver('GLOP')

x = solver.NumVar(0, solver.infinity(), 'x')
y = solver.NumVar(0, solver.infinity(), 'y')

solver.Maximize(3 * x + 4 * y)

solver.Add(x + 2 * y <= 14)
solver.Add(3 * x - y >= 0)
solver.Add(x - y <= 2)

solver.Solve()

print("Optimal value =", solver.Objective().Value())
print("x =", x.solution_value(), "y =", y.solution_value())

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🎲 2. Basic Probability

# Probability of at least one head in 2 tosses
p = 1 - (0.5 ** 2)
print(p)

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♟️ 3. Game Trees, Minimax & Alpha-Beta

Minimax

def minimax(depth, is_max):
    if depth == 0:
        return 1

    if is_max:
        return max(minimax(depth-1, False), minimax(depth-1, False))
    else:
        return min(minimax(depth-1, True), minimax(depth-1, True))

Alpha-Beta Pruning

def alphabeta(depth, alpha, beta, maximizing):
    if depth == 0:
        return 1

    if maximizing:
        value = -float('inf')
        value = max(value, alphabeta(depth-1, alpha, beta, False))
        alpha = max(alpha, value)
        if alpha >= beta:
            return value
        return value
    else:
        value = float('inf')
        value = min(value, alphabeta(depth-1, alpha, beta, True))
        beta = min(beta, value)
        if beta <= alpha:
            return value
        return value

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📊 4. Exploratory Data Analysis (EDA)

import pandas as pd

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

print(df.head())
print(df.info())
print(df.describe())

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🧹 5. Data Cleaning & Preprocessing

Handling Missing Values

df.fillna(df.mean(), inplace=True)

Encoding

df = pd.get_dummies(df, columns=['category'])

Feature Scaling

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df[['col']] = scaler.fit_transform(df[['col']])

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📏 6. Evaluation Metrics

from sklearn.metrics import accuracy_score, precision_score

accuracy = accuracy_score(y_true, y_pred)
precision = precision_score(y_true, y_pred)

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📈 7. Linear & Logistic Regression

Linear Regression

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X, y)

Logistic Regression

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
model.fit(X, y)

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🌳 8. Decision Trees

from sklearn.tree import DecisionTreeClassifier

model = DecisionTreeClassifier()
model.fit(X, y)

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📍 9. K-Nearest Neighbors (KNN)

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier(n_neighbors=3)
model.fit(X, y)

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🔵 10. K-Means Clustering

from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3)
kmeans.fit(X)

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📉 11. Plotting & Visualization

import matplotlib.pyplot as plt

plt.scatter(df['x'], df['y'])
plt.title("Scatter Plot")
plt.show()

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🧪 Lab Structure

Lab	Content
Lab 7	OR-Tools & Optimization
Lab 8	Probability & Game Trees
Lab 9	Exploratory Data Analysis
Lab 10	Data Cleaning & Preprocessing
Lab 11	Machine Learning Models
Lab 12	Evaluation & Visualization

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🚀 Setup & Run

pip install -r requirements.txt
python main.py

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📁 Folder Structure

AI-Labs/
│── data/
│── notebooks/
│── src/
│── README.md
│── requirements.txt

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📌 Notes

• This repository is for learning and academic purposes.
• Each lab builds upon the previous one.
• Code is kept simple for clarity and understanding.

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⭐ Contribution

Feel free to fork, improve, and submit pull requests!