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Commit c9f86b2

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ICTAcadamy
1 parent 02f9982 commit c9f86b2

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7 files changed

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7 files changed

+130
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‎ICTAcadamy/Week-1/BreastCancer.py

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import numpy as np
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from sklearn import datasets
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from sklearn.model_selection import train_test_split, GridSearchCV
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from sklearn.svm import SVC
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from sklearn.metrics import accuracy_score
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breast_cancer = datasets.load_breast_cancer()
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X = breast_cancer.data
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y = breast_cancer.target
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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svm = SVC(kernel='rbf')
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param_grid = {
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'C': [0.1, 1, 10],
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'gamma': [0.001, 0.01, 0.1, 1]
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}
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grid_search = GridSearchCV(estimator=svm, param_grid=param_grid, cv=5)
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grid_search.fit(X_train, y_train)
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best_params = grid_search.best_params_
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best_model = grid_search.best_estimator_
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y_pred = best_model.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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print(accuracy)

‎ICTAcadamy/Week-1/DataFrame.py

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import pandas as pd
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data={'a':[1,2],'b':[3,4],'c':[5,6]}
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df=pd.DataFrame(data)
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def float_value(x):
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return x*1.0
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df=df.apply(float_value)
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print(df)

‎ICTAcadamy/Week-1/LogisticRegression.py

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import numpy as np
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import pandas as pd
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from sklearn.datasets import load_breast_cancer
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from sklearn.feature_selection import RFE
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from sklearn.linear_model import LogisticRegression
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breast_cancer = load_breast_cancer()
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X = breast_cancer.data
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y = breast_cancer.target
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feature_names = breast_cancer.feature_names
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df = pd.DataFrame(X, columns=feature_names)
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model = LogisticRegression()
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num_features_to_select = 5
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rfe = RFE(estimator=model, n_features_to_select=num_features_to_select)
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rfe.fit(X, y)
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selected_features = df.columns[rfe.support_]
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feature_ranking = rfe.ranking_
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feature_rank_df = pd.DataFrame({'Feature': df.columns, 'Ranking': feature_ranking})
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sorted_feature_rank_df = feature_rank_df.sort_values(by='Ranking')
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print("Top {} Features:".format(num_features_to_select))
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print(sorted_feature_rank_df.head(num_features_to_select))

‎ICTAcadamy/Week-1/MergeFrame.py

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import pandas as pd
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df1 = pd.DataFrame([['a', 1, 2], ['b', 2, 3], ['c', 4, 5]], columns=['A', 'B', 'C'])
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df2 = pd.DataFrame([['a', 6, 7], ['a', 8, 9]], columns=['A', 'D', 'E'])
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merged_df = df1.merge(df2, on='A', how='left')
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print(merged_df)

‎ICTAcadamy/Week-1/SKlearn.py

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from sklearn.metrics import precision_score, recall_score, confusion_matrix
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x=[1,0,0,1,1]
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y=[0,1,0,1,0]
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precision=precision_score(x,y)
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recall=recall_score(x,y)
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print("Precision API : ",precision);
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print("Recall API : ",recall);
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mat=confusion_matrix(x,y)
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true_negative=mat[0,0]
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true_positive=mat[1,1]
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false_negative=mat[0,1]
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false_positive=mat[1,0]
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precision=true_positive/(true_positive+false_positive)
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recall=true_positive/(true_positive+false_negative)
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print("Precision Confusion Matrix : ",precision);
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print("Recall Confusion Matrix : ",recall);

‎ICTAcadamy/Week-2/NaturalLangToolKit.py

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import nltk
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from nltk import word_tokenize, pos_tag
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nltk.download("punkt")
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nltk.download("averaged_perceptron_tagger")
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sentence = "The quick brown fox jumps over the lazy dog."
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words = word_tokenize(sentence)
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print(pos_tag(words))

‎ICTAcadamy/Week-2/Vectorization.py

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import pandas as pd
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from sklearn.feature_extraction.text import TfidfVectorizer
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documents = [
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"I am Nithin.",
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"K Ramakrishnan Collge of Technology.",
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"Fourth Year Student.",
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"Consider me as Document Data",
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]
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a = TfidfVectorizer()
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tfidf_matrix = a.fit_transform(documents)
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feature_names = a.get_feature_names_out()
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out = pd.DataFrame(data=tfidf_matrix.toarray(), columns=feature_names)
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print(out)

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