# Tensorflow House Prediction Using Linear Regression

## Understand Regression and Train your first model

In the previous article, we have read in-depth about depth in tensors their rank & data type. We have also built a simple computation graph and under tensor basic operation. If you haven't read the article here's the link.

In this tutorial we are going to cover, linear regression and more in-depth about this also we are going to build a house price prediction App.

- What is Linear Regression?
- Understand using Example of Linear Regression?
- Understanding Learning Algorithm
- Cost Function
- Gradient Descent
- Predicting house price using Tensorflow
- Cost and the Optimization function

## What is Linear Regression?

Linear regression is used for finding the **linear relationship** between *target* and *one or more predictors*.

In another word:

Linear Regression is a Linear Model. This means we will establish a linear relationship between the `input variables(X)`

and `single output variable(Y)`

. When the *input(X)* is a single variable this model is called **Simple Linear Regression** and when there are *multiple input variables(X)*, it is called *Multiple Linear Regression.*

## Understand using Example

Let's see a simple **example of linear regression** and how it works in TensorFlow.
Here, we solve a simple equation **[y=m*x+b]**. We will calculate the `slope(m)`

and the `intercept(b)`

of the line that best fits our data.

The following are the steps to calculate the values of `m`

and `b`

.

### Step 1. Setting Artificial Data for Regression

Below is the code to create random test data that is linearly separated:

```
import numpy as np
x_data = np.linspace(0, 10, 10) + np.random.uniform(-1.5, 1.5, 10)
array([-0.69756846 2.24332136 0.87019185 2.91653533 4.87247308 6.14932119
6.61805361 8.68002133 9.38586681 8.80601073])
y_label = np.linspace(0, 10, 10) + np.random.uniform(-1.5, 1.5, 10)
array([-0.39423666 0.68045758 1.83709626 3.82504931 3.74358699 4.82393256
8.15763383 8.28064161 9.81634308 10.71215334])
```

Here, we generate ten evenly spaced numbers between 0 and 10 and another ten random values between -1.5 and 1.5. Then, we add these values.

### Step 2. Plot the data

If we plot the above data, this is how it would look:

```
import matplotlib.pyplot as plt
plt.plot(x_data, y_label, '*')
```

Now, we want to find the best fit (equation of a line) for the given data points.

### Step 3. Assign the Variables

Now we're going to assign the TensorFlow variable using `tf.Variable()`

.

```
np.random.rand(2)
array([0.34873631 0.88758771])
# We will use upper random value in m,b
m = tf.Variable(0.34)
b = tf.Variable(0.88)
```

Here, we have assigned variables `m`

and `b`

randomly using a **Numpy random function**.

### Step 4. Apply Cost Function

The cost function is basically the **error between** the *actual value* and the *calculated value*. We'll read more in-depth later in the tutorial.

Let's find out the cost function:

```
error = 0
for x,y in zip(x_data, y_label):
y_hat = m*x + b
# Our predicted value
error += (y - y_hat)**2
# The cost we want to minimize
# We'll need to use optimization function the minimization
```

### Step 5. Apply Optimization Function

For training purposes, you need to use an optimizer.

**1. Apply the Optimization Function**

```
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001)
train = optimizer.minimize(error)
```

**2. Initialize the variables**

```
init = tf.global_variables_initializer()
```

**3. Create the session and run the computation**

```
with tf.Session() as sess:
sess.run(init)
epochs = 100
for i in range(epochs):
sess.run(train)
# Fetch back results
final_scope, final_intercept = sess.run([m, b])
print(final_scope)
print(final_intercept)
# Output
1.0795033
0.43419915
```

In this case, it is a gradient descent optimizer, and we need to specify the learning rate.

### 6. Evaluate the Results

The last step is used to plot the model, i.e., the best-fit line. You can use the plot method to plot the best-fit line.

```
x_test = np.linspace(-1, 11, 10)
y_pred_plot = final_scope * x_test + final_intercept
plt.plot(x_test, y_pred_plot, 'r')
plt.plot(x_data, y_label, '*')
```

You can see that the line of **best fit is passing in between all the data points**. If you consider any specific location and *calculate the error, it is minimal*. This is how you **evaluate the results**.

## Understanding Learning Algorithm

Broadly, there are 3 types of Machine Learning Algorithms

**Supervised Learning:**This algorithm consists of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Decision Tree, Regression, Random Forest,**Unsupervised Learning:**In this algorithm, we do not have any target or outcome variable to predict / estimate. Apriori Algorithm, K-means**Reinforcement Learning:**Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. Markov Decision Process

**List of Common Algorithms used in industry:**

- Linear Regression
- Decision Tree
- Support Vector Machine (SVM)
- Naive Bayes
- KNN
- K-Means
- Random Forest
- Gradient Boosting Algorithms

## Cost Function

One common function that is often used is **mean squared error**, which measures the *difference between the actual value from the dataset and the estimated value (the prediction)*.

We can adjust the equation a little to make the calculation a little more simple.

Here is the **summary**,

- The
`hypothesis h(x)`

defines the linear model with parameters`θo`

and`θ1`

. - The
**cost function quantifies**how good the parameters are. Poor*prediction leads to a high value*of**cost function**.

## Gradient Descent

Gradient descent is an **optimization algorithm** used to find the values of parameters (coefficients) of a function (f) that **minimizes a cost function (cost)**.

Gradient descent is best used when the parameters cannot be calculated analytically (e.g. using linear algebra) and must be searched for by an optimization algorithm.

## Predicting House Price using Tensorflow

Data Source: github/officialvoltry/.../day_3_housing.csv

**California Housing Prices**

The data contains information from the 1990 California census. The columns are as follows:

- longitude
- latitude
- housingMedianAge
- totalRooms
- totalBedrooms
- population
- households
- medianIncome
- oceanProximity

Let's get started by importing Libraries (Recommend to use Jupyter )

You can full source code here ->

- Import Libraries
`import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline`

Load the dataset

`df=pd.read_csv(“housing.csv”) df.head()`

Data Analysis

`df.info()`

Scaling and Train Test Split

`X = df.drop(‘median_house_value’,axis=1) y = df[‘median_house_value’] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)`

Scaling

`from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_train= scaler.fit_transform(X_train) X_test = scaler.transform(X_test)`

Creating a Model

```
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Dropout
model = Sequential()
model.add(Dense(8,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(3,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
```

- Training the Model
`from tensorflow.keras.callbacks import EarlyStopping early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10) model.fit(x=X_train,y=y_train.values, validation_data=(X_test,y_test.values), batch_size=128,epochs=400, callbacks=[early_stop])`

Plotting graph

`losses = pd.DataFrame(model.history.history) losses.plot()`

Evaluation

```
from sklearn.metrics import mean_squared_error,mean_absolute_error
predictions = model.predict(X_test)
mean_absolute_error(y_test,predictions)
# 125709.1601435053
np.sqrt(mean_squared_error(y_test,predictions))
# 165928.57353834526
```

Next: In the next tutorial, we will read Recurrent Neural Network(RNN)

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