100_Days_of_code_challenge

I have taken a challenge to write code for consecutive 100 days. In this time period I will code in Python language about Machine Learning, data wrangling, data visualization, feature engineering, etc.

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100_Days_of_code_challenge

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Name		Name	Last commit message	Last commit date
Latest commit History 196 Commits
.ipynb_checkpoints		.ipynb_checkpoints
Dataset		Dataset
__pycache__		__pycache__
figures		figures
visual_aids		visual_aids
Day_100_geospatial_map.ipynb		Day_100_geospatial_map.ipynb
Day_10_Calculus_for_Machine_Learning_1.ipynb		Day_10_Calculus_for_Machine_Learning_1.ipynb
Day_11_Calculus_for_Machine_Learning_2.ipynb		Day_11_Calculus_for_Machine_Learning_2.ipynb
Day_12_Hyperparameter_Optimization_With_Random_Search_and_Grid_Search.ipynb		Day_12_Hyperparameter_Optimization_With_Random_Search_and_Grid_Search.ipynb
Day_13_Bayesian_Optimization.ipynb		Day_13_Bayesian_Optimization.ipynb
Day_14_Titanic_dataset_Visualization.ipynb		Day_14_Titanic_dataset_Visualization.ipynb
Day_15_Titanic_dataset_Preprocessing.ipynb		Day_15_Titanic_dataset_Preprocessing.ipynb
Day_16_Titanic_dataset_Survival_Prediction.ipynb		Day_16_Titanic_dataset_Survival_Prediction.ipynb
Day_17(a)_Fine_tuning_Titanic_prediction.ipynb		Day_17(a)_Fine_tuning_Titanic_prediction.ipynb
Day_17(b)_Filtering_a_Time_series_to_detect_gravitational_waves.ipynb		Day_17(b)_Filtering_a_Time_series_to_detect_gravitational_waves.ipynb
Day_18_Titanic_dataset.ipynb		Day_18_Titanic_dataset.ipynb
Day_19_Breast_Cancer_Dataset.ipynb		Day_19_Breast_Cancer_Dataset.ipynb
Day_1_An_object-oriented_perceptron_API.ipynb		Day_1_An_object-oriented_perceptron_API.ipynb
Day_20_Breast_Cancer_Dataset_Modification.ipynb		Day_20_Breast_Cancer_Dataset_Modification.ipynb
Day_22_Global_warming.ipynb		Day_22_Global_warming.ipynb
Day_23_Pipeline.ipynb		Day_23_Pipeline.ipynb
Day_24_Ensemble_Error.ipynb		Day_24_Ensemble_Error.ipynb
Day_25_majority_vote_classifier.ipynb		Day_25_majority_vote_classifier.ipynb
Day_26_StratifiedKFold.ipynb		Day_26_StratifiedKFold.ipynb
Day_27_Debugging algorithms with learning curves.ipynb		Day_27_Debugging algorithms with learning curves.ipynb
Day_28_hyperparameter_search_with_successive_halving.ipynb		Day_28_hyperparameter_search_with_successive_halving.ipynb
Day_29_correlation_matrix.ipynb		Day_29_correlation_matrix.ipynb
Day_2_training_a_perceptron_model.ipynb		Day_2_training_a_perceptron_model.ipynb
Day_30_majority_voting_classifier.ipynb		Day_30_majority_voting_classifier.ipynb
Day_31_pipeline.ipynb		Day_31_pipeline.ipynb
Day_32_clustering.ipynb		Day_32_clustering.ipynb
Day_33_Centroids_in_clustering_analysis .ipynb		Day_33_Centroids_in_clustering_analysis .ipynb
Day_34_silhouette_plots.ipynb		Day_34_silhouette_plots.ipynb
Day_35_week01_assignment_AML.ipynb		Day_35_week01_assignment_AML.ipynb
Day_36_hierarchical_tree.ipynb		Day_36_hierarchical_tree.ipynb
Day_37_Attaching_dendrograms_to_a_heat_map.ipynb		Day_37_Attaching_dendrograms_to_a_heat_map.ipynb
Day_38_few-competative_codes.ipynb		Day_38_few-competative_codes.ipynb
Day_39_DBSCAN.ipynb		Day_39_DBSCAN.ipynb
Day_3_Classification_with_MIniboone.ipynb		Day_3_Classification_with_MIniboone.ipynb
Day_40_Bagging_Classifier_on_Wine_dataset.ipynb		Day_40_Bagging_Classifier_on_Wine_dataset.ipynb
Day_41_Bagging_Classifier_comparison_with_DecisionTree_classifier_on_Wine_dataset.ipynb		Day_41_Bagging_Classifier_comparison_with_DecisionTree_classifier_on_Wine_dataset.ipynb
Day_42_AdaBoost.ipynb		Day_42_AdaBoost.ipynb
Day_43_xgboost_decisiontree_on_wine_dataset.ipynb		Day_43_xgboost_decisiontree_on_wine_dataset.ipynb
Day_44_animate_a_function.ipynb		Day_44_animate_a_function.ipynb
Day_45_Preprocessing_the_movie_dataset_into_more_convenient_format.ipynb		Day_45_Preprocessing_the_movie_dataset_into_more_convenient_format.ipynb
Day_46_bag_of_word_models.ipynb		Day_46_bag_of_word_models.ipynb
Day_47_countvectorizer_and_TfidfTransformer.ipynb		Day_47_countvectorizer_and_TfidfTransformer.ipynb
Day_48_cleaning_the_text_data.ipynb		Day_48_cleaning_the_text_data.ipynb
Day_49_tokenize_and_stemming.ipynb		Day_49_tokenize_and_stemming.ipynb
Day_4_implementing_Adaline.ipynb		Day_4_implementing_Adaline.ipynb
Day_50_stop_words.ipynb		Day_50_stop_words.ipynb
Day_51_Training_a_Logistic_Regression_Model_for_Document_Classification-.ipynb		Day_51_Training_a_Logistic_Regression_Model_for_Document_Classification-.ipynb
Day_52_out_of_core_learning.ipynb		Day_52_out_of_core_learning.ipynb
Day_53_LDA.ipynb		Day_53_LDA.ipynb
Day_54.ipynb		Day_54.ipynb
Day_55.ipynb		Day_55.ipynb
Day_56.ipynb		Day_56.ipynb
Day_57_evaluating_the_performance_of_linear_regression_models.ipynb		Day_57_evaluating_the_performance_of_linear_regression_models.ipynb
Day_58_Polynomial_term_using_sklearn.ipynb		Day_58_Polynomial_term_using_sklearn.ipynb
Day_59.ipynb		Day_59.ipynb
Day_5_Adaline_with_Iris_data.ipynb		Day_5_Adaline_with_Iris_data.ipynb
Day_60_Decision_Tree_Regressor.ipynb		Day_60_Decision_Tree_Regressor.ipynb
Day_61_Random_Forest_Regressor.ipynb		Day_61_Random_Forest_Regressor.ipynb
Day_62.ipynb		Day_62.ipynb
Day_63.ipynb		Day_63.ipynb
Day_64.ipynb		Day_64.ipynb
Day_65.ipynb		Day_65.ipynb
Day_66.ipynb		Day_66.ipynb
Day_67.ipynb		Day_67.ipynb
Day_68.ipynb		Day_68.ipynb
Day_69.ipynb		Day_69.ipynb
Day_6_SGD.ipynb		Day_6_SGD.ipynb
Day_70.ipynb		Day_70.ipynb
Day_71_massaging_datasets_using_pandas.ipynb		Day_71_massaging_datasets_using_pandas.ipynb
Day_73.ipynb		Day_73.ipynb
Day_74.ipynb		Day_74.ipynb
Day_75_pandabasics_ex_1.ipynb		Day_75_pandabasics_ex_1.ipynb
Day_76_pandabasics_ex_2_and_3.ipynb		Day_76_pandabasics_ex_2_and_3.ipynb
Day_77_Rank_transformation.ipynb		Day_77_Rank_transformation.ipynb
Day_78_feature_generation_from_datetime_features.ipynb		Day_78_feature_generation_from_datetime_features.ipynb
Day_79_Encoding.ipynb		Day_79_Encoding.ipynb
Day_7_Training_a_Perceptron.ipynb		Day_7_Training_a_Perceptron.ipynb
Day_80_LSTM_for_univariate_time_series_forecasting.ipynb		Day_80_LSTM_for_univariate_time_series_forecasting.ipynb
Day_81_Creating_DataFrames.ipynb		Day_81_Creating_DataFrames.ipynb
Day_82_Making_Pandas_DataFrames_from_API_Requests.ipynb		Day_82_Making_Pandas_DataFrames_from_API_Requests.ipynb
Day_83_Inspecting_subsetting_adding_removing_data_in_a_DataFrame_Object.ipynb		Day_83_Inspecting_subsetting_adding_removing_data_in_a_DataFrame_Object.ipynb
Day_84_Filtering_DataFrame.ipynb		Day_84_Filtering_DataFrame.ipynb
Day_85_Wide_vs_Long_format_data.ipynb		Day_85_Wide_vs_Long_format_data.ipynb
Day_86_Collecting_temperature_data_from_an_API.ipynb		Day_86_Collecting_temperature_data_from_an_API.ipynb
Day_87_data_cleaning.ipynb		Day_87_data_cleaning.ipynb
Day_88_Assignment_Pandas_Operations.ipynb		Day_88_Assignment_Pandas_Operations.ipynb
Day_89_sorting_the_dataframe.ipynb		Day_89_sorting_the_dataframe.ipynb
Day_8_Algorithm_for_Logistic_regression.ipynb		Day_8_Algorithm_for_Logistic_regression.ipynb
Day_90_reshaping_the_dataset.ipynb		Day_90_reshaping_the_dataset.ipynb
Day_91_Potential_lead_dataset_with_LGBM.ipynb		Day_91_Potential_lead_dataset_with_LGBM.ipynb
Day_92_Handling_duplicate_missing_or_invalid_data.ipynb		Day_92_Handling_duplicate_missing_or_invalid_data.ipynb
Day_93_taking_a_subset_of_fb_dataset.ipynb		Day_93_taking_a_subset_of_fb_dataset.ipynb
Day_94_filtering_the_fb_dataset.ipynb		Day_94_filtering_the_fb_dataset.ipynb
Day_95_using_query_and_agg_functions.ipynb		Day_95_using_query_and_agg_functions.ipynb
Day_96_adding_a_new_item_in_a_list_of_dictionary.ipynb		Day_96_adding_a_new_item_in_a_list_of_dictionary.ipynb

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100_Days_of_code_challenge

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