Jupyter notebook

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No. Code
0 # Libraries
1 

    import csv

    import pandas as pd

    import numpy as np

    import matplotlib.pyplot as plt

    import seaborn as sn

    import matplotlib.mlab as mlab

    from matplotlib.pyplot import figure, show
2 # Loading Dataframe
3 

    dataframe = pd.read_csv('train.csv')
4 # Describe dataframe
5 

    dataframe.describe()
6 # Dataframe
7 

    dataframe
8 # Preparacion del dataframe
9 

    df_x = dataframe[['x']]

    df_y = dataframe['y']
10 # Normalizar dataframe
11 

    df_nor_x = (df_x - df_x.mean())/df_x.std()
12 # x
13 

    df_nor_x
14 # y
15 

    df_nor_y = (df_y - df_y.mean())/df_y.std()

    df_nor_y
16 # Dataframe de entrenamiento y de prueba
17 

    x_train, x_test, y_train, y_test = train_test_split(df_nor_x,df_nor_y,test_size=0.3,random_state=42)
18 # Model de regresion lineal
19 

    model = LinearRegression()
20 # Entrenamiento del model
21 

    model.fit(x_train,y_train)
22 # Prueba del modelo
23 

    predictions = model.predict(x_test)
24 # Evaluacion del modelo
25 # Coefficients
26 

    model.coef_
27 # Independent term
28 

    model.intercept_
29 # Mean squared error
30 

    mean_squared_error(y_test, predictions)
31 # Mean absolute error
32 

    mean_absolute_error(y_test, predictions)
33 # Variance
34 

    r2_score(y_test, predictions)
35 # Comparacion de los resultados
36 

    compare = pd.DataFrame({'Actual':y_test, 'Predicted':predictions})
37 # Valores de prueba
38 

    compare.Actual.head(10)
39 # Valores predichos
40 

    compare.Predicted.head(10)
41 # Grafica scatter
42 

    plt.scatter(y_test,predictions)
43 # Grafica de distribucion
44 

    sn.distplot(y_test - predictions)
45 # Preparacion del dataframe
46 

    df_x = dataframe[['x']]

    df_y = dataframe['y']
47 # Normalizar dataframe
48 

    df_nor_x = (df_x - df_x.mean())/df_x.std()
49 # x
50 

    df_nor_x
51 # y
52 

    df_nor_y = (df_y - df_y.mean())/df_y.std()

    df_nor_y
53 # Dataframe de entrenamiento y de prueba
54 

    x_train, x_test, y_train, y_test = train_test_split(df_nor_x,df_nor_y,test_size=0.3,random_state=42)
55 # Model de regresion lineal
56 

    model = LinearRegression()
57 # Entrenamiento del model
58 

    model.fit(x_train,y_train)
59 # Prueba del modelo
60 

    predictions = model.predict(x_test)
61 # Evaluacion del modelo
62 # Coefficients
63 

    model.coef_
64 # Independent term
65 

    model.intercept_
66 # Mean squared error
67 

    mean_squared_error(y_test, predictions)
68 # Mean absolute error
69 

    mean_absolute_error(y_test, predictions)
70 # Variance
71 

    r2_score(y_test, predictions)
72 # Comparacion de los resultados
73 

    compare = pd.DataFrame({'Actual':y_test, 'Predicted':predictions})
74 # Valores de prueba
75 

    compare.Actual.head(10)
76 # Valores predichos
77 

    compare.Predicted.head(10)
78 # Grafica scatter
79 

    plt.scatter(y_test,predictions)
80 # Grafica de distribucion
81 

    sn.distplot(y_test - predictions)
82 # Preparacion del dataframe
83 

    df_x = dataframe[['x']]

    df_y = dataframe['y']
84 # Normalizar dataframe
85 

    df_nor_x = (df_x - df_x.mean())/df_x.std()
86 # x
87 

    df_nor_x
88 # y
89 

    df_nor_y = (df_y - df_y.mean())/df_y.std()

    df_nor_y
90 # Dataframe de entrenamiento y de prueba
91 

    x_train, x_test, y_train, y_test = train_test_split(df_nor_x,df_nor_y,test_size=0.3,random_state=42)
92 # Model de regresion lineal
93 

    model = LinearRegression()
94 # Entrenamiento del model
95 

    model.fit(x_train,y_train)
96 # Prueba del modelo
97 

    predictions = model.predict(x_test)
98 # Evaluacion del modelo
99 # Coefficients
100 

    model.coef_
101 # Independent term
102 

    model.intercept_
103 # Mean squared error
104 

    mean_squared_error(y_test, predictions)
105 # Mean absolute error
106 

    mean_absolute_error(y_test, predictions)
107 # Variance
108 

    r2_score(y_test, predictions)
109 # Comparacion de los resultados
110 

    compare = pd.DataFrame({'Actual':y_test, 'Predicted':predictions})
111 # Valores de prueba
112 

    compare.Actual.head(10)
113 # Valores predichos
114 

    compare.Predicted.head(10)
115 # Grafica scatter
116 

    plt.scatter(y_test,predictions)
117 # Grafica de distribucion
118 

    sn.distplot(y_test - predictions)
119 # Preparacion del dataframe
120 

    df_x = dataframe[['x']]

    df_y = dataframe['y']
121 # Normalizar dataframe
122 

    df_nor_x = (df_x - df_x.mean())/df_x.std()
123 # x
124 

    df_nor_x
125 # y
126 

    df_nor_y = (df_y - df_y.mean())/df_y.std()

    df_nor_y
127 # Dataframe de entrenamiento y de prueba
128 

    x_train, x_test, y_train, y_test = train_test_split(df_nor_x,df_nor_y,test_size=0.3,random_state=42)
129 # Model de regresion lineal
130 

    model = LinearRegression()
131 # Entrenamiento del model
132 

    model.fit(x_train,y_train)
133 # Prueba del modelo
134 

    predictions = model.predict(x_test)
135 # Evaluacion del modelo
136 # Coefficients
137 

    model.coef_
138 # Independent term
139 

    model.intercept_
140 # Mean squared error
141 

    mean_squared_error(y_test, predictions)
142 # Mean absolute error
143 

    mean_absolute_error(y_test, predictions)
144 # Variance
145 

    r2_score(y_test, predictions)
146 # Comparacion de los resultados
147 

    compare = pd.DataFrame({'Actual':y_test, 'Predicted':predictions})
148 # Valores de prueba
149 

    compare.Actual.head(10)
150 # Valores predichos
151 

    compare.Predicted.head(10)
152 # Grafica scatter
153 

    plt.scatter(y_test,predictions)
154 # Grafica de distribucion
155 

    sn.distplot(y_test - predictions)
156 # Describe
157 

    dataframe.describe()
158 # Heatmap nulls
159 

    sn.heatmap(dataframe.isnull())