def post(self, request):
form = mlr_regression()
if request.method == 'POST' or 'FILES':
form = mlr_regression(request.POST, request.FILES)
if form.is_valid():
handle_uploaded_file(request.FILES['upload'])
x1_ind = form.cleaned_data['col_start']
x2_ind = form.cleaned_data['col_end']
y1_dep = form.cleaned_data['col_y']
file_name = 'C:\\Users\\ambuj\\Desktop\\mltool\\mltool\\static\\csv\\' + request.FILES[
'upload'].name
dataset = pd.read_csv(file_name)
if (x1_ind == x2_ind):
X = dataset.iloc[:, x1_ind - 1].values
else:
X = dataset.iloc[:, x1_ind - 1:x2_ind - 1].values
y = dataset.iloc[:, y1_dep - 1].values
X = np.array(X).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)
from sklearn.tree import DecisionTreeRegressor
regressor = DecisionTreeRegressor()
regressor.fit(X, y)
text = form.cleaned_data['prediction']
y_pred1 = regressor.predict(text)
form = mlr_regression()
a = X.tolist()
b = y.tolist()
l = []
l1 = []
for i in range(0, len(a)):
l.append(a[i][0])
for i in range(0, len(b)):
l1.append(b[i][0])
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color='red')
plt.plot(X_grid, regressor.predict(X_grid), color='blue')
plt.title('Decission Tree Regression')
plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/test.jpeg')
plt.gcf().clear()
args = {'form': form, 'result': y_pred1[0], 'x': l, 'y': l1}
return render(request, self.template_name, args)
def post(self, request):
form = mlr_regression()
if request.method == 'POST' or 'FILES':
form = IntForm(request.POST, request.FILES)
if form.is_valid():
handle_uploaded_file(request.FILES['upload'])
x1_ind = form.cleaned_data['col_start']
x2_ind = form.cleaned_data['col_end']
y1_dep = form.cleaned_data['col_y']
file_name = 'C:\\Users\\ambuj\\Desktop\\mltool\\mltool\\static\\csv\\' + request.FILES[
'upload'].name
dataset = pd.read_csv(file_name)
if (x1_ind == x2_ind):
X = dataset.iloc[:, x1_ind - 1].values
else:
X = dataset.iloc[:, x1_ind - 1:x2_ind - 1].values
y = dataset.iloc[:, y1_dep - 1].values
X = np.array(X).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)
from sklearn.svm import SVR
regressor = SVR(kernel='rbf')
regressor.fit(X, y)
text = form.cleaned_data['prediction']
y_pred1 = regressor.predict(text)
form = mlr_regression()
a = X.tolist()
b = y.tolist()
l = []
l1 = []
for i in range(0, len(a)):
l.append(a[i][0])
for i in range(0, len(b)):
l1.append(b[i][0])
args = {'form': form, 'result': y_pred1[0], 'x': l, 'y': l1}
return render(request, self.template_name, args)
def post(self, request):
form = IntForm()
if request.method == 'POST' or 'FILES':
form = IntForm(request.POST, request.FILES)
if form.is_valid():
handle_uploaded_file(request.FILES['upload'])
x1_ind = form.cleaned_data['col_start']
y1_dep = form.cleaned_data['col_y']
file_name = 'C:\\Users\\ambuj\\Desktop\\mltool\\mltool\\static\\csv\\' + request.FILES[
'upload'].name
dataset = pd.read_csv(file_name)
X = dataset.iloc[:, x1_ind - 1].values
y = dataset.iloc[:, y1_dep - 1].values
X = np.array(X).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=1 / 3, random_state=0)
# Fitting Simple Linear Regression to Training Set
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
text = form.cleaned_data['prediction']
y_pred1 = regressor.predict(text)
form = IntForm()
a = X_train.tolist()
b = y_pred.tolist()
l = []
l1 = []
for i in range(0, len(a)):
l.append(a[i][0])
for i in range(0, len(b)):
l1.append(b[i][0])
am = json.dumps(l)
ll1 = json.dumps(l1)
plt.scatter(X_train, y_train, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Linear Regression')
plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/train.jpeg')
plt.gcf().clear()
plt.scatter(X_test, y_test, color='red')
plt.plot(X_train, regressor.predict(X_train), color='blue')
plt.title('Linear Regression')
plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/test.jpeg')
plt.gcf().clear()
args = {
'form': form,
'result': y_pred1[0][0],
'x_train': l,
'y_pred': l1,
'll1': ll1,
'am': am
}
return render(request, self.template_name, args)
def post(self, request):
form = polynomial_regression()
if request.method == 'POST' or 'FILES':
form = polynomial_regression(request.POST, request.FILES)
if form.is_valid():
handle_uploaded_file(request.FILES['upload'])
x1_ind = form.cleaned_data['col_start']
x2_ind = form.cleaned_data['col_end']
y1_dep = form.cleaned_data['col_y']
deg = form.cleaned_data['degree'] #degree ka dabba bnana h
file_name = 'C:\\Users\\ambuj\\Desktop\\mltool\\mltool\\static\\csv\\' + request.FILES[
'upload'].name
dataset = pd.read_csv(file_name)
if (x1_ind == x2_ind):
X = dataset.iloc[:, x1_ind - 1].values
else:
X = dataset.iloc[:, x1_ind - 1:x2_ind - 1].values
y = dataset.iloc[:, y1_dep - 1].values
X = np.array(X).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree=deg)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
text = form.cleaned_data['prediction']
y_pred1 = lin_reg_2.predict(poly_reg.fit_transform(text))
form = polynomial_regression()
# a = X.tolist()
# b = y.tolist()
# l = []
# l1 = []
# for i in range(0, len(a)):
# l.append(a[i][0])
# for i in range(0, len(b)):
# l1.append(b[i][0])
X_grid = np.arange(min(X), max(X), 0.1)
X_grid = X_grid.reshape((len(X_grid), 1))
plt.scatter(X, y, color='red')
plt.plot(X,
lin_reg_2.predict(poly_reg.fit_transform(X)),
color='blue')
plt.title('Polynomial Regression')
plt.xlabel('Independent Variable')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/test.jpeg')
plt.gcf().clear()
args = {'form': form, 'result': y_pred1[0][0]}
return render(request, self.template_name, args)
def post(self, request):
form = mlr_regression()
if request.method == 'POST' or 'FILES':
form = mlr_regression(request.POST, request.FILES)
if form.is_valid():
handle_uploaded_file(request.FILES['upload'])
x1_ind = form.cleaned_data['col_start']
x2_ind = form.cleaned_data['col_end']
y1_dep = form.cleaned_data['col_y']
file_name = 'C:\\Users\\ambuj\\Desktop\\mltool\\mltool\\static\\csv\\' + request.FILES[
'upload'].name
dataset = pd.read_csv(file_name)
X = dataset.iloc[:, x1_ind - 1:x2_ind - 1].values
y = dataset.iloc[:, y1_dep - 1].values
# X = np.array(X).reshape(-1, 1)
# y = np.array(y).reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=0)
# Fitting Multiple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
# Predicting the Test set results
y_pred = regressor.predict(X_test)
list = []
for i in range(0, 3):
text = form.cleaned_data['prediction']
list.append(text)
y_pred1 = regressor.predict([list])
# y_pred2=regressor.predict()
# y_pred3= regressor.predict()
form = mlr_regression()
plt.scatter(X_train[:, 0], y_train, color='red')
# plt.plot(X_train[:,0], regressor.predict(X_train[:,0]), color = 'blue')
plt.title('MLR')
plt.xlabel('Independent Variable 1')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/train1.jpeg')
plt.gcf().clear()
# plt.switch_backend('qt4agg')
plt.scatter(X_train[:, 1], y_train, color='red')
# plt.plot(X_train[:,0], regressor.predict(X_train[:,0]), color = 'blue')
plt.title('MLR')
plt.xlabel('Independent Variable 2')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/train2.jpeg')
plt.gcf().clear()
plt.scatter(X_train[:, 2], y_train, color='red')
# plt.plot(X_train[:,0], regressor.predict(X_train[:,0]), color = 'blue')
plt.title('MLR')
plt.xlabel('Independent Variable 3')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/train3.jpeg')
plt.gcf().clear()
# Visualising the Test set results
plt.scatter(X_test[:, 0], y_test, color='red')
# plt.plot(X_train[:,0], regressor.predict(X_train[:,0]), color = 'blue')
plt.title('MLR')
plt.xlabel('Independent Variable 1')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/test1.jpeg')
plt.gcf().clear()
# plt.switch_backend('qt4agg')
plt.scatter(X_test[:, 1], y_test, color='red')
# plt.plot(X_train[:,0], regressor.predict(X_train[:,0]), color = 'blue')
plt.title('MLR')
plt.xlabel('Independent Variable 2')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/test2.jpeg')
plt.gcf().clear()
plt.scatter(X_test[:, 2], y_test, color='red')
# plt.plot(X_train[:,0], regressor.predict(X_train[:,0]), color = 'blue')
plt.title('MLR')
plt.xlabel('Independent Variable 3')
plt.ylabel('Dependent Variable')
plt.savefig('mltool/static/csv/test3.jpeg')
plt.gcf().clear()
args = {'form': form, 'result': y_pred1[0]}
return render(request, self.template_name, args)