saimadhu-polamuri
/ email-spam-confusion-matrix-graphs.py
Created
July 19, 2020 17:38
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| # Creating the confusion matrix graphs | |
| cf_train_matrix = confusion_matrix(y_train, train_predictions) | |
| plt.figure(figsize=(10,8)) | |
| sns.heatmap(cf_train_matrix, annot=True, fmt='d') | |
| cf_test_matrix = confusion_matrix(y_test, test_predictions) | |
| plt.figure(figsize=(10,8)) | |
| sns.heatmap(cf_test_matrix, annot=True, fmt='d') |
saimadhu-polamuri
/ email-spam-classifier-with-spacy.py
Created
July 19, 2020 17:52
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| """ | |
| =============================================== | |
| Objective: Building email classifier with spacy | |
| Author: saimadhu.polamuri | |
| Blog: dataaspirant.com | |
| Date: 2020-07-17 | |
| =============================================== | |
| """ |
saimadhu-polamuri
/ confusion_matrix_sklearn_python
Created
August 2, 2020 15:13
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| """ | |
| =============================================== | |
| Objective: Implementing confusion matrix in different ways. | |
| Author: saimadhu.polamuri | |
| Blog: https://dataaspirant.com | |
| Date: 2020-08-02 | |
| =============================================== | |
| """ | |
saimadhu-polamuri
/ evaluation-metrics-for-classification-models.py
Last active
August 6, 2020 14:14
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| """ | |
| =============================================== | |
| Objective: Building email classifier with spacy | |
| Author: sharmila.polamuri | |
| Blog: https://dataaspirant.com | |
| Date: 2020-08-05 | |
| =============================================== | |
| """ | |
| # =========================================== |
saimadhu-polamuri
/ handle_imbalance_data.py
Created
August 9, 2020 09:58
The complete code for the article in https://dataaspirant.com
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| """ | |
| =============================================== | |
| Objective: Handling imbalance data | |
| Author: Jaiganesh Nagidi | |
| Blog: https://dataaspirant.com | |
| Date: 2020-08-09 | |
| =============================================== | |
| """ | |
saimadhu-polamuri
/ markov-chain-mante-carlo-simulation.py
Created
August 13, 2020 07:11
Code for the article published in https://dataaspirant.com
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| """ | |
| =============================================== | |
| Objective: Implementing Markov Chains model | |
| Author: Venkatesh Nagilla | |
| Blog: https://dataaspirant.com | |
| Date: 2020-08-09 | |
| =============================================== | |
| """ | |
| import pymc3 as pm |
saimadhu-polamuri
/ dataaspirant-handle-overfitting-deep-learning-moons-dataset.py
Created
August 24, 2020 03:42
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| ## Moons dataset | |
| import numpy as np | |
| from sklearn.datasets import make_moons | |
| np.random.seed(800) | |
| x, y = make_moons(n_samples=100, noise=0.2, random_state=1) | |
| # plot the graph | |
| import matplotlib.pyplot as plt |
saimadhu-polamuri
/ dataaspirant-handle-overfitting-deep-learning-model-creation.py
Created
August 24, 2020 03:51
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| # importing libraries | |
| import tensorflow as tf | |
| import warnings | |
| from mlxtend.plotting import plot_decision_regions | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense | |
| from sklearn.model_selection import train_test_split | |
| x_train,x_test,y_train,y_test = train_test_split( |
saimadhu-polamuri
/ dataaspirant-handle-overfitting-deep-learning-plot-train-test-loss.py
Created
August 24, 2020 03:56
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| ## Plot train and test loss | |
| plt.plot(history.history['loss'], label='train') | |
| plt.plot(history.history['val_loss'], label='test') | |
| plt.legend() | |
| plt.show() |
saimadhu-polamuri
/ dataaspirant-handle-overfitting-deep-learning-Regularization-model.py
Created
August 24, 2020 04:09
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| ## Model after applying Regularization techniques | |
| model = Sequential() | |
| model.add(Dense(500, input_dim=2, activation='relu',kernel_regularizer='l2')) | |
| model.add(Dense(1, activation='sigmoid',kernel_regularizer='l2')) | |
| model.compile(loss='binary_crossentropy', | |
| optimizer='adam', | |
| metrics=['accuracy']) | |
| history = model.fit(x_train, y_train, | |
| validation_data=(x_test, y_test), |