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)