Added Scikit Learn Module

Scikit-Learn Module/Readme.md

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+# Scikit-Learn
+
+> Unlock the Power of Machine Learning with Scikit-learn: Simplifying Complexity, Empowering Discovery
+
+
+**Supervised Learning**
+-   Linear Models
+
+-   Support Vector Machines
+
+-   Data Preprocessing
+
+1. Linear Models
+
+The following are a set of
+methods intended for regression in which the target value is expected to
+be a linear combination of the features. In mathematical notation, if
+$\hat{y}$ is the predicted value.
+
+$$
+\hat{y}(w, x) = w_0 + w_1 + \ldots + w_p
+$$
+
+Across the module, we designate the vector w =
+$(w_0, w_1, \ldots, w_n)$ as `coef_` and $w_0$ as `intercept_`.
+
+
+- *Linear Regression*
+  Linear Regression fits a linear model with coefficients w = $(w_0 ,w_1 ,
+...w_n)$ to minimize the residual sum of squares between the observed
+targets in the dataset, and the targets predicted by the linear
+approximation. Mathematically it solves a problem of the form:
+
+    $\min_{w} || X w - y||_2^2$
+
+``` python
+from sklearn import linear_model
+reg = linear_model.LinearRegression() #To Use Linear Regression
+reg.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
+coefficients = reg.coef_
+intercept = reg.intercept_
+
+print("Coefficients:", coefficients)
+print("Intercept:", intercept)
+```
+
+Output:
+
+    Coefficients: [0.5 0.5]
+    Intercept: 1.1102230246251565e-16
+
+
+![LinearRegression](https://scikit-learn.org/stable/_images/sphx_glr_plot_ols_001.png)
+
+This is how the Linear Regression fits the line .
+
+
+- Support Vector Machines
+  Support vector machines (SVMs) are a set of supervised learning methods
+used for classification, regression and outliers detection.
+
+*The advantages of support vector machines are:*
+
+Effective in high dimensional spaces.
+
+Still effective in cases where number of dimensions is greater than the
+number of samples.
+
+Uses a subset of training points in the decision function (called
+support vectors), so it is also memory efficient.
+
+Versatile: different Kernel functions can be specified for the decision
+function. Common kernels are provided, but it is also possible to
+specify custom kernels.
+
+*The disadvantages of support vector machines include:*
+
+If the number of features is much greater than the number of samples,
+avoid over-fitting in choosing Kernel functions and regularization term
+is crucial.
+
+SVMs do not directly provide probability estimates, these are calculated
+using an expensive five-fold cross-validation (see Scores and
+probabilities, below).
+
+The support vector machines in scikit-learn support both dense
+(numpy.ndarray and convertible to that by numpy.asarray) and sparse (any
+scipy.sparse) sample vectors as input. However, to use an SVM to make
+predictions for sparse data, it must have been fit on such data. For
+optimal performance, use C-ordered numpy.ndarray (dense) or
+scipy.sparse.csr_matrix (sparse) with dtype=float64
+
+**Linear Kernel:**
+
+Function: 𝐾 ( 𝑥 , 𝑦 ) = 𝑥 𝑇 𝑦
+
+Parameters: No additional parameters.
+
+**Polynomial Kernel:**
+
+Function: 𝐾 ( 𝑥 , 𝑦 ) = ( 𝛾 𝑥 𝑇 𝑦 𝑟 ) 𝑑
+
+Parameters:
+
+γ (gamma): Coefficient for the polynomial term. Higher values increase
+the influence of high-degree polynomials.
+
+r: Coefficient for the constant term.
+
+d: Degree of the polynomial.
+
+**Radial Basis Function (RBF) Kernel:**
+
+Function: 𝐾 ( 𝑥 , 𝑦 ) = exp ⁡ ( − 𝛾 ∣ ∣ 𝑥 − 𝑦 ∣ ∣ 2 )
+
+Parameters: 𝛾 γ (gamma): Controls the influence of each training
+example. Higher values result in a more complex decision boundary.
+
+**Sigmoid Kernel:**
+
+Function: 𝐾 ( 𝑥 , 𝑦 ) = tanh ⁡ ( 𝛾 𝑥 𝑇 𝑦 𝑟 )
+
+Parameters:
+
+γ (gamma): Coefficient for the sigmoid term.
+
+r: Coefficient for the constant term.
+
+
+``` python
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import svm, datasets
+
+# Load example dataset (Iris dataset)
+iris = datasets.load_iris()
+X = iris.data[:, :2]  # We only take the first two features
+y = iris.target
+
+# Define the SVM model with RBF kernel
+C = 1.0  # Regularization parameter
+gamma = 0.7  # Kernel coefficient
+svm_model = svm.SVC(kernel='rbf', C=C, gamma=gamma)
+
+# Train the SVM model
+svm_model.fit(X, y)
+
+# Plot the decision boundary
+x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02),
+                     np.arange(y_min, y_max, 0.02))
+Z = svm_model.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+
+plt.contourf(xx, yy, Z, cmap=plt.cm.Paired, alpha=0.8)
+
+# Plot the training points
+plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)
+plt.xlabel('Sepal length')
+plt.ylabel('Sepal width')
+plt.title('SVM with RBF Kernel')
+plt.show()
+```
+![SVM](https://github.com/AmrutaJayanti/codeharborhub/assets/142327526/24bc053e-54b6-4702-a442-d7f6e4b34332)
+
+- Data Preprocessing
+  Data preprocessing is a crucial step in the machine learning pipeline
+that involves transforming raw data into a format suitable for training
+a model. Here are some fundamental techniques in data preprocessing
+using scikit-learn:
+
+**Handling Missing Values:**
+
+Imputation: Replace missing values with a calculated value (e.g., mean,
+median, mode) using SimpleImputer. Removal: Remove rows or columns with
+missing values using dropna.
+
+**Feature Scaling:**
+
+Standardization: Scale features to have a mean of 0 and a standard
+deviation of 1 using StandardScaler.
+
+Normalization: Scale features to a range between 0 and 1 using
+MinMaxScaler. Encoding Categorical Variables:
+
+One-Hot Encoding: Convert categorical variables into binary vectors
+using OneHotEncoder.
+
+Label Encoding: Encode categorical variables as integers using
+LabelEncoder.
+
+**Feature Transformation:**
+
+Polynomial Features: Generate polynomial features up to a specified
+degree using PolynomialFeatures.
+
+Log Transformation: Transform features using the natural logarithm to
+handle skewed distributions.
+
+**Handling Outliers:**
+
+Detection: Identify outliers using statistical methods or domain
+knowledge. Transformation: Apply transformations (e.g., winsorization)
+or remove outliers based on a threshold.
+
+**Handling Imbalanced Data:**
+
+Resampling: Over-sample minority class or under-sample majority class to
+balance the dataset using techniques like RandomOverSampler or
+RandomUnderSampler.
+
+Synthetic Sampling: Generate synthetic samples for the minority class
+using algorithms like Synthetic Minority Over-sampling Technique
+(SMOTE). Feature Selection:
+
+Univariate Feature Selection: Select features based on statistical tests
+like ANOVA using SelectKBest or SelectPercentile.
+
+Recursive Feature Elimination: Select features recursively by
+considering smaller and smaller sets of features using RFECV.
+
+**Splitting Data:**
+
+Train-Test Split: Split the dataset into training and testing sets using
+train_test_split.
+
+Cross-Validation: Split the dataset into multiple folds for
+cross-validation using KFold or StratifiedKFold.