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added my code in 'machine_learning' and 'neural_network' folder #10243

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31faa5f
added my code in and folder
IBR-41379 Oct 10, 2023
bb40ab3
Appropriated the name of the existing program according to the repo i…
IBR-41379 Oct 10, 2023
578b159
Rename Simple_Adaline.py to simple_adaline.py
IBR-41379 Oct 10, 2023
0154cd4
Chaged the name of yhe folder according to the convention.
IBR-41379 Oct 10, 2023
c08165b
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 10, 2023
5f373c1
Update simple_adaline.py
IBR-41379 Oct 10, 2023
b3a7404
Rename Churn_Modelling.csv to churn_modelling.csv
IBR-41379 Oct 10, 2023
d5af854
Update churn_cal.py
IBR-41379 Oct 10, 2023
669cf9d
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 10, 2023
df219a0
Update simple_adaline.py
IBR-41379 Oct 10, 2023
7dd3fb5
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 10, 2023
537d850
Update churn_cal.py
IBR-41379 Oct 10, 2023
38a8a0b
Update simple_adaline.py
IBR-41379 Oct 10, 2023
e0f8aa1
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 10, 2023
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[pre-commit.ci] auto fixes from pre-commit.com hooks 
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pre-commit-ci[bot] committed Oct 10, 2023
commit c08165bc22058fb53011433d43cf092912777408
19 changes: 14 additions & 5 deletions machine_learning/decision_tree_churn/churn_cal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,13 +15,22 @@
# print(df.head())

# Sorting the dependent and independent values
x = df[['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts',
'HasCrCard', 'IsActiveMember', 'EstimatedSalary']].values
y = df['Exited']
x = df[
[
"CreditScore",
"Age",
"Tenure",
"Balance",
"NumOfProducts",
"HasCrCard",
"IsActiveMember",
"EstimatedSalary",
]
].values
y = df["Exited"]

# Splitting the dataset into training and testing data
x_test, x_train, y_test, y_train = train_test_split(
x, y, test_size=0.3, random_state=3)
x_test, x_train, y_test, y_train = train_test_split(x, y, test_size=0.3, random_state=3)

# Creating the decision tree classifier
decTree = DecisionTreeClassifier(criterion="entropy", max_depth=4)
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: decTree
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44 changes: 22 additions & 22 deletions neural_network/simple_adaline.py
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@@ -1,51 +1,51 @@
def wght_cng_or(wgt, T, al):
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As there is no test file in this pull request nor any test function or class in the file neural_network/simple_adaline.py, please provide doctest for the function wght_cng_or

Please provide return type hint for the function: wght_cng_or. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: wgt

Please provide type hint for the parameter: T

Please provide descriptive name for the parameter: T

Please provide type hint for the parameter: al
O = wgt[0]*0+wgt[1]*0
O = wgt[0] * 0 + wgt[1] * 0
if O <= T:
Ol = wgt[0]*0+wgt[1]*1
Ol = wgt[0] * 0 + wgt[1] * 1
if Ol >= T:
Ole = wgt[0]*1+wgt[1]*0
Ole = wgt[0] * 1 + wgt[1] * 0
if Ole >= T:
Ola = wgt[0]*1+wgt[1]*1
Ola = wgt[0] * 1 + wgt[1] * 1
if Ola >= T:
return wgt
else:
wgt[0] = wgt[0]+al*1*1
wgt[1] = wgt[1]+al*1*1
wgt[0] = wgt[0] + al * 1 * 1
wgt[1] = wgt[1] + al * 1 * 1
return wght_cng_or(wgt, T, al)
else:
wgt[0] = wgt[0]+al*1*1
wgt[1] = wgt[1]+al*1*0
wgt[0] = wgt[0] + al * 1 * 1
wgt[1] = wgt[1] + al * 1 * 0
return wght_cng_or(wgt, T, al)
else:
wgt[0] = wgt[0]+al*1*0
wgt[1] = wgt[1]+al*1*1
wgt[0] = wgt[0] + al * 1 * 0
wgt[1] = wgt[1] + al * 1 * 1
return wght_cng_or(wgt, T, al)
else:
T += al
return wght_cng_or(wgt, T, al)


def wght_cng_and(wgt, T, al):
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As there is no test file in this pull request nor any test function or class in the file neural_network/simple_adaline.py, please provide doctest for the function wght_cng_and

Please provide return type hint for the function: wght_cng_and. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: wgt

Please provide type hint for the parameter: T

Please provide descriptive name for the parameter: T

Please provide type hint for the parameter: al
O = wgt[0]*0+wgt[1]*0
O = wgt[0] * 0 + wgt[1] * 0
if O <= T:
Ol = wgt[0]*0+wgt[1]*1
Ol = wgt[0] * 0 + wgt[1] * 1
if Ol <= T:
Ole = wgt[0]*1+wgt[1]*0
Ole = wgt[0] * 1 + wgt[1] * 0
if Ole <= T:
Ola = wgt[0]*1+wgt[1]*1
Ola = wgt[0] * 1 + wgt[1] * 1
if Ola >= T:
return wgt
else:
wgt[0] = wgt[0]+(al*1*1)
wgt[1] = wgt[1]+(al*1*1)
wgt[0] = wgt[0] + (al * 1 * 1)
wgt[1] = wgt[1] + (al * 1 * 1)
return wght_cng_and(wgt, T, al)
else:
wgt[0] = wgt[0]-(al*1*1)
wgt[1] = wgt[1]-(al*1*0)
wgt[0] = wgt[0] - (al * 1 * 1)
wgt[1] = wgt[1] - (al * 1 * 0)
return wght_cng_and(wgt, T, al)
else:
wgt[0] = wgt[0]-(al*1*0)
wgt[1] = wgt[1]-(al*1*1)
wgt[0] = wgt[0] - (al * 1 * 0)
wgt[1] = wgt[1] - (al * 1 * 1)
return wght_cng_and(wgt, T, al)
else:
T += al
Expand All @@ -54,7 +54,7 @@ def wght_cng_and(wgt, T, al):

def and_gate(wgt, A, B, T, al):
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As there is no test file in this pull request nor any test function or class in the file neural_network/simple_adaline.py, please provide doctest for the function and_gate

Please provide return type hint for the function: and_gate. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: wgt

Please provide type hint for the parameter: A

Please provide descriptive name for the parameter: A

Please provide type hint for the parameter: B

Please provide descriptive name for the parameter: B

Please provide type hint for the parameter: T

Please provide descriptive name for the parameter: T

Please provide type hint for the parameter: al
wgt = wght_cng_and(wgt, T, al)
O = wgt[0]*A+wgt[1]*B
O = wgt[0] * A + wgt[1] * B
if O >= T:
return 1
else:
Expand All @@ -63,7 +63,7 @@ def and_gate(wgt, A, B, T, al):

def or_gate(wgt, A, B, T, al):
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As there is no test file in this pull request nor any test function or class in the file neural_network/simple_adaline.py, please provide doctest for the function or_gate

Please provide return type hint for the function: or_gate. If the function does not return a value, please provide the type hint as: def function() -> None:

Please provide type hint for the parameter: wgt

Please provide type hint for the parameter: A

Please provide descriptive name for the parameter: A

Please provide type hint for the parameter: B

Please provide descriptive name for the parameter: B

Please provide type hint for the parameter: T

Please provide descriptive name for the parameter: T

Please provide type hint for the parameter: al
wgt = wght_cng_or(wgt, T, al)
O = wgt[0]*A+wgt[1]*B
O = wgt[0] * A + wgt[1] * B
if O >= T:
return 1
else:
Expand Down