# My Linear Regression Model predicting falsely

Please everyone, i am very new to Machine learning and i just tried out linear regression and i’m not getting accurate result from my model.

Aim:
Predict the salary of an employee based on his/her number of experience.

Excel sheet content:

Years of Experience | Salaries
10 | 300000
2 | 20000
8 | 599000
9 | 290000
1 | 20000
7 | 80000
4 | 700000

``````# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import pandas as pd

features_X = datasets.iloc[:, :-1].values
features_Y = datasets.iloc[:, 1:].values

features_X = torch.from_numpy(features_X).float()
features_Y = torch.from_numpy(features_Y).float()

print(datasets, "\n\n", features_X, "\n\n", features_Y, "\n\n", features_X.type(), "\n\n", features_Y.type())

x_train = torch.FloatTensor(features_X)
y_train = torch.FloatTensor(features_Y)

""" Define out Hyper-parameters """
learning_rate = 0.0001
epochs = 500

""" Building the Network """

class Model(nn.Module):
def __init__(self, input_layer, output_layer):
super(Model, self).__init__()
self.linear = nn.Linear(input_layer, output_layer)

def forward(self, x):
y_pred = self.linear(x)
return y_pred

model = Model(input_layer=1, output_layer=1)
cost_func = nn.MSELoss(size_average=False)
optim = torch.optim.SGD(model.parameters(), lr=learning_rate)

for epoch in range(epochs):
y_pred = model(x_train)
loss = cost_func(y_pred, y_train)
print("traning: ", loss.data)