TECH VS ME

  Code of Project for Jupyter notebook: import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score raw_data = r"Set your csv file path" data = pd.read_csv(raw_data, delimiter=';') print("Dataset loaded successfully!") print(data.head()) # Check for missing values print("Missing values:\n", data.isnull().sum()) # Remove duplicates data = data.drop_duplicates() print("Data shape after removing duplicates:", data.shape) # Separate features and target X = data.drop('quality', axis=1) y = data['quality'] # Feature scaling scaler = StandardScaler() X_scaled = scaler.fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(     X_scaled, y, test_size=0...

Wine Quality Prediction Project (Red Wine Variant) Code of project:   For code click here 1. Dataset Source: Dataset: Wine Quality Dataset (Red Wine Variant) Source: UCI Machine Learning Repository Link: https://archive.ics.uci.edu/ml/datasets/Wine+Quality Direct CSV Link: https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv 2. Dataset Selection: Why This Dataset? This dataset is ideal for  regression-based machine learning  tasks because: 1.       Real-World Relevance : Predicts wine quality using measurable chemical properties, mimicking industry needs. 2.       Structured & Clean : No missing values, minimal preprocessing (e.g., scaling, duplicate removal). 3.       Educational Value : Small size and clear features make it perfect for practicing workflows (preprocessing → modeling...