{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd  \nimport numpy as np \nfrom sklearn import discriminant_analysis\n\nimport os\n\n#Linear Discriminant Analysis\n\ndf_train = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\") #Reads training data csv file into dataframe.\ndf_test = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\") #Reads test data csv file to dataframe.\ndigit_recognizer_trainX = df_train.iloc[:,1:] #Drops label column (first column) of training data and puts all pixel data into dataframe. To be used as X values for LDA.\ndigit_recognizer_trainY = df_train.iloc[:,:1] #Takes label column of training data. To be used as Y values for LDA.\nregr = discriminant_analysis.LinearDiscriminantAnalysis() #Linear discriminant analysis object.\n\nregr.fit(digit_recognizer_trainX, np.array(digit_recognizer_trainY).ravel()) #Fits training data.\ndigit_recognizer_predY = pd.DataFrame(regr.predict(df_test), columns=['Label']) #regr.predict(df_test) plugs test data into linear model and returns array of predicted labels. We then convert this into a dataframe and label the column 'Label'. \n\ndigit_recognizer_predY.insert(0, 'ImageId', range(1,len(digit_recognizer_predY)+1)) #Creates the ImageID column required in the digit recognizer problem.\n\n#digit_recognizer_predY.loc[digit_recognizer_predY.Label < 0,'Label'] = 0 #Changes value in predictions to 0 if less than 0.\n#digit_recognizer_predY.loc[digit_recognizer_predY.Label > 9,'Label'] = 9 #Changes value in predictions to 9 if greater than 9.\n\nprint(digit_recognizer_predY)\n\ndigit_recognizer_predY.to_csv(r'LDAdigit_recognizer_predY.csv', index = False, header=True) #Saves prediction dataframe to csv file.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}