something

app.py

@@ -7,32 +7,13 @@ from sklearn.decomposition import PCA
 import joblib
 import pandas as pd
 
-def load_data():
-    # Load data from CSV files
-    df = pd.read_csv('slump_test.data.csv')
-    df.drop(columns=['SLUMP(cm)'], inplace=True)
-    df.dropna(inplace=True)
-    # df = pd.get_dummies(df, drop_first=True)
-    # numeric_columns = df.select_dtypes(include=['int64', 'float64']).columns
-    # # Creating a MinMaxScaler object
-    # scaler = MinMaxScaler()
-    # # Normalizing numeric features
-    # df[numeric_columns] = scaler.fit_transform(df[numeric_columns])
-    # scaler = StandardScaler()
-    # df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
-    
-    # # Perform PCA
-    # pca = PCA()
-    # df_pca = pca.fit_transform(df_scaled)
-    
-    # Calculate explained variance
-    # explained_variance = pca.explained_variance_ratio_
-    
-    
+def load_data():    
+    X_train = pd.read_csv('slump_test.data.csv').values[:,:-1]
+    # X_train = df.iloc[:,:-1]
+    y_train = pd.read_csv('slump_test.data.csv').values[:, -1]
+    # y_train = df.iloc[:, -1] 
 
-    X_train = df.iloc[:,:-1]
     print("X_train",X_train.shape)
-    y_train = df.iloc[:, -1] 
     print("y_train",y_train.shape)
     
 

preprocessed_slump.csv

@@ -0,0 +1,104 @@
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slump_test.data.csv

@@ -1,104 +0,0 @@
-Cement,Slag,Fly ash,Water,SP,Coarse Aggr.,Fine Aggr.,SLUMP(cm),FLOW(cm),Compressive Strength (28-day)(Mpa)
-273,82,105,210,9,904,680,23,62,34.99
-163,149,191,180,12,843,746,0,20,41.14
-162,148,191,179,16,840,743,1,20,41.81
-162,148,190,179,19,838,741,3,21.5,42.08
-154,112,144,220,10,923,658,20,64,26.82
-147,89,115,202,9,860,829,23,55,25.21
-152,139,178,168,18,944,695,0,20,38.86
-145,0,227,240,6,750,853,14.5,58.5,36.59
-152,0,237,204,6,785,892,15.5,51,32.71
-304,0,140,214,6,895,722,19,51,38.46
-145,106,136,208,10,751,883,24.5,61,26.02
-148,109,139,193,7,768,902,23.75,58,28.03
-142,130,167,215,6,735,836,25.5,67,31.37
-354,0,0,234,6,959,691,17,54,33.91
-374,0,0,190,7,1013,730,14.5,42.5,32.44
-159,116,149,175,15,953,720,23.5,54.5,34.05
-153,0,239,200,6,1002,684,12,35,28.29
-295,106,136,206,11,750,766,25,68.5,41.01
-310,0,143,168,10,914,804,20.5,48.2,49.3
-296,97,0,219,9,932,685,15,48.5,29.23
-305,100,0,196,10,959,705,20,49,29.77
-310,0,143,218,10,787,804,13,46,36.19
-148,180,0,183,11,972,757,0,20,18.52
-146,178,0,192,11,961,749,18,46,17.19
-142,130,167,174,11,883,785,0,20,36.72
-140,128,164,183,12,871,775,23.75,53,33.38
-308,111,142,217,10,783,686,25,70,42.08
-295,106,136,208,6,871,650,26.5,70,39.4
-298,107,137,201,6,878,655,16,26,41.27
-314,0,161,207,6,851,757,21.5,64,41.14
-321,0,164,190,5,870,774,24,60,45.82
-349,0,178,230,6,785,721,20,68.5,43.95
-366,0,187,191,7,824,757,24.75,62.7,52.65
-274,89,115,202,9,759,827,26.5,68,35.52
-137,167,214,226,6,708,757,27.5,70,34.45
-275,99,127,184,13,810,790,25.75,64.5,43.54
-252,76,97,194,8,835,821,23,54,33.11
-165,150,0,182,12,1023,729,14.5,20,18.26
-158,0,246,174,7,1035,706,19,43,34.99
-156,0,243,180,11,1022,698,21,57,33.78
-145,177,227,209,11,752,715,2.5,20,35.66
-154,141,181,234,11,797,683,23,65,33.51
-160,146,188,203,11,829,710,13,38,33.51
-291,105,0,205,6,859,797,24,59,27.62
-298,107,0,186,6,879,815,3,20,30.97
-318,126,0,210,6,861,737,17.5,48,31.77
-280,92,118,207,9,883,679,25.5,64,37.39
-287,94,121,188,9,904,696,25,61,43.01
-332,0,170,160,6,900,806,0,20,58.53
-326,0,167,174,6,884,792,21.5,42,52.65
-320,0,163,188,9,866,776,23.5,60,45.69
-342,136,0,225,11,770,747,21,61,32.04
-356,142,0,193,11,801,778,8,30,36.46
-309,0,142,218,10,912,680,24,62,38.59
-322,0,149,186,8,951,709,20.5,61.5,45.42
-159,193,0,208,12,821,818,23,50,19.19
-307,110,0,189,10,904,765,22,40,31.5
-313,124,0,205,11,846,758,22,49,29.63
-143,131,168,217,6,891,672,25,69,26.42
-140,128,164,237,6,869,656,24,65,29.5
-278,0,117,205,9,875,799,19,48,32.71
-288,0,121,177,7,908,829,22.5,48.5,39.93
-299,107,0,210,10,881,745,25,63,28.29
-291,104,0,231,9,857,725,23,69,30.43
-265,86,111,195,6,833,790,27,60,37.39
-159,0,248,175,12,1041,683,21,51,35.39
-160,0,250,168,12,1049,688,18,48,37.66
-166,0,260,183,13,859,827,21,54,40.34
-320,127,164,211,6,721,723,2,20,46.36
-336,134,0,222,6,756,787,26,64,31.9
-276,90,116,180,9,870,768,0,20,44.08
-313,112,0,220,10,794,789,23,58,28.16
-322,116,0,196,10,818,813,25.5,67,29.77
-294,106,136,207,6,747,778,24,47,41.27
-146,106,137,209,6,875,765,24,67,27.89
-149,109,139,193,6,892,780,23.5,58.5,28.7
-159,0,187,176,11,990,789,12,39,32.57
-261,78,100,201,9,864,761,23,63.5,34.18
-140,1.4,198.1,174.9,4.4,1049.9,780.5,16.25,31,30.83
-141.1,0.6,209.5,188.8,4.6,996.1,789.2,23.5,53,30.43
-140.1,4.2,215.9,193.9,4.7,1049.5,710.1,24.5,57,26.42
-140.1,11.8,226.1,207.8,4.9,1020.9,683.8,21,64,26.28
-160.2,0.3,240,233.5,9.2,781,841.1,24,75,36.19
-140.2,30.5,239,169.4,5.3,1028.4,742.7,21.25,46,36.32
-140.2,44.8,234.9,171.3,5.5,1047.6,704,23.5,52.5,33.78
-140.5,61.1,238.9,182.5,5.7,1017.7,681.4,24.5,60,30.97
-143.3,91.8,239.8,200.8,6.2,964.8,647.1,25,55,27.09
-194.3,0.3,240,234.2,8.9,780.6,811.3,26.5,78,38.46
-150.4,110.9,239.7,168.1,6.5,1000.2,667.2,9.5,27.5,37.92
-150.3,111.4,238.8,167.3,6.5,999.5,670.5,14.5,36.5,38.19
-155.4,122.1,240,179.9,6.7,966.8,652.5,14.5,41.5,35.52
-165.3,143.2,238.3,200.4,7.1,883.2,652.6,17,27,32.84
-303.8,0.2,239.8,236.4,8.3,780.1,715.3,25,78,44.48
-172,162.1,238.5,166,7.4,953.3,641.4,0,20,41.54
-172.8,158.3,239.5,166.4,7.4,952.6,644.1,0,20,41.81
-184.3,153.4,239.2,179,7.5,920.2,640.9,0,20,41.01
-215.6,112.9,239,198.7,7.4,884,649.1,27.5,64,39.13
-295.3,0,239.9,236.2,8.3,780.3,722.9,25,77,44.08
-248.3,101,239.1,168.9,7.7,954.2,640.6,0,20,49.97
-248,101,239.9,169.1,7.7,949.9,644.1,2,20,50.23
-258.8,88,239.6,175.3,7.6,938.9,646,0,20,50.5
-297.1,40.9,239.9,194,7.5,908.9,651.8,27.5,67,49.17
-348.7,0.1,223.1,208.5,9.6,786.2,758.1,29,78,48.77