DefinitionScikit-learn is an open source Python library that implements a range of machine learning, preprocessing, cross-validation and visualization algorithms using a unified interface |
Splitting Datafrom sklearn.model_selection import train_test_split | X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=7) |
Handling Missing Datafrom sklearn.impute import SimpleImputer
| missingvalues = SimpleImputer(missing_values = np.nan, strategy = 'mean')
missingvalues = missingvalues.fit(X[:, 1:3])
X[:, 1:3]=missingvalues.transform(X[:, 1:3]) |
Linear Regressionfrom sklearn.linear_model import LinearRegression | linear_reg = LinearRegression()
linear_reg.fit( X , y ) |
Decision Tree and Random forestfrom sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor | regressor = DecisionTreeRegressor(random_state = 0)
regressor.fit(X,y) | regressor2 = RandomForestRegressor(n_estimators = 100,random_state=0)
regressor2.fit(X,y) |
Cross-Validationfrom sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_validate | X , y = make)regression(n_samples = 1000, random_state = 0)
lr = LinearRegression() | result = cross_validate(lr,X,y)
result['test_score'] |
It is used to know the effectiveness of our Models by re-sampling and applying to models in different iterations. Pandas functions for importing Datapd.read_csv(filename) | From a CSV file | pd.read_excel(filename) | From an Excel file | pd.read_sql(query, connection_object) | Read from a SQL table/database | pd.read_clipboard() | Takes the contents of your clipboard and passes it to read_table() |
Visualization using Scikit-learnfrom sklearn.metrics import plot_roc_curve | Importing "plot_roc_curve" to plot | svc_disp = plot_roc_curve(svc, X_test, y_test) | Plotting Receiver operating characteristic Curve | metrics.plot_confusion_matrix | Plotting Confusion Matrix. |
Clustering metricsAdjusted Rand Index
>>> from sklearn.metrics import adjusted_rand_score
>>> adjusted_rand_score(y_true, y_pred)
| Homogeneity
>>> from sklearn.metrics import homogeneity_score >>> homogeneity_score(y_true, y_pred) | V-measure
>>> from sklearn.metrics import v_measure_score
>>> metrics.v_measure_score(y_true, y_pred) |
Pandas Data Cleaning functionspd.isnull() | Checks for null Values, Returns Boolean Arrray | pd.notnull() | Opposite of pd.isnull() | df.dropna() | Drop all rows that contain null values | df.dropna(axis=1) | Drop all columns that contain null values | df.fillna(x) | Replace all null values with x |
Numpy Basic Functionsimport numpy as np | importing numpy | example = [0,1,2]
example = np.array(example) | array([0, 1, 2]) | np.arange(1,4) | array([1,2,3]) | np.zeros(2,2) | array([[0,0],[0,0]]) | np.linspace(0,10,2) | array([0,5]), gives two evenly spaced values | np.eye(2) | array([[1,0],[0,1]), 2*2 Identity Matrix | example.reshape(3,1) | array([[0],[1],[2]]) |
| | Loading Dataset from local Machineimport pandas as pd | data = pd.read_csv(pathname) |
If the file is in the local directory then we can directly use File name Loading Data from Standard datasetsfrom sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits() |
Encoding Categorical Variablesfrom sklearn.preprocessing import LabelEncoder | labelencoder_X = LabelEncoder() | X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0 ]) | onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray() |
Polynomial Regressionfrom sklearn.preprocessing import PolynomialFeatures | poly_reg = PolynomialFeatures(degree =2)
X_poly = poly_reg.fit_transform(X) |
It not only checks the relation between X(independent) and y(dependent). But also checks with X2 ..X n. (n is degree specified by us). Evaluation of Regression Model PerformanceR2 = 1 - SS(residuals)/SS(total) | SS(res) = SUM(Yi - y^i)2
SS(Total) = SUM(yi - yavg)2 | from sklearn.metrics import r2_score
r2_score(y_true,y_pred) |
The Greater the R2 value the better the model is.. Converting Dataframe to Matrixdata = pd.read_csv("data.csv") | X = data.iloc[ : , :-1].values | y = data.iloc[ : , 3].values |
Feature Scalingfrom sklearn.preprocessing import StandardScaler | sc_X = StandardScaler() | X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test) |
Euclidean distance is dominated by the larger numbers and to make all the values on the same scale. hence Scaling should be done. Most of the models do feature scaling by themselves. SVR(Non-linear Regression model)from sklearn.svm import SVR | regressor = SVR(kernel = 'rbf')
regressor.fit(X,y) | y_prediction = regressor.predict(values) |
Basically, the kernel is selected based on the given problem. If the problem is Linear then kernel='linear'. And if problem is non-linear we can choose either 'poly' or 'rbf'(gussian) Some Classification ModelsLogistic Regression | K-NN(K- nearest neighbours) | Support Vector Machine(SVM) | Naive Bayes | Decision Tree Classification | Random Forest Classification |
Some Clustering ModelsK-Means Clustering | Hierarchial Clustering | DB-SCAN |
Knowing about Data information with Pandasdf.head(n) | First n rows of the DataFrame | df.tail(n) | Last n rows of the DataFrame | df.shape | Number of rows and columns | df.info() | Index, Datatype and Memory information | df.describe() | Summary statistics for numerical columns |
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