House prices prediction - Duplicate

test123

$ 12321+123 $

$$ 1+1 $$

$ 420>44 $

!pip install seaborn

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import mpl_toolkits %matplotlib inline

data = pd.read_csv("kc_house_data.csv")

Instead of using data.head() and data.describe(), we can use the variable explorer

What can we infer from the above describe function ?

Look at the bedroom columns , the dataset has a house where the house has 33 bedrooms , seems to be a massive house and would be interesting to know more about it as we progress.
Maximum square feet is 13,450 where as the minimum is 290. we can see that the data is distributed.

Similarly , we can infer so many things by just looking at the describe function.

Now , we are going to see some visualization and also going to see how and what can we infer from visualization.

Which is the most common house (Bedroom wise) ?

Let’s see which is most common bedroom number. You may wonder why is it important ? Let’s look at this problem from a builder’s perspective, sometimes it’s important for a builder to see which is the highest selling house type which enables the builder to make house based on that. Here in India , for a good locality a builder opts to make houses which are more than 3 bedrooms which attracts the higher middle class and upper class section of the society.

Let’s see how this pans out for this data ?

data['bedrooms'].value_counts().plot(kind='bar') plt.title('number of Bedroom') plt.xlabel('Bedrooms') plt.ylabel('Count') sns.despine

As we can see from the visualization 3 bedroom houses are most commonly sold followed by 4 bedroom. So how is it useful ? For a builder having this data , He can make a new building with more 3 and 4 bedroom’s to attract more buyers.

So now we know that 3 and 4 bedroom’s are highest selling. But at which locality ?

Visualizing the location of the houses based on latitude and longitude.

So according to the dataset , we have latitude and longitude on the dataset for each house. We are going to see the common location and how the houses are placed.

plt.figure(figsize=(10,10)) sns.jointplot(x=data.lat.values, y=data.long.values, size=10) plt.ylabel('Longitude', fontsize=12) plt.xlabel('Latitude', fontsize=12) plt.show() # plt1 = plt() sns.despine

We use seaborn , and we get his beautiful visualization. Joinplot function helps us see the concentration of data and placement of data and can be really useful. Let us see what we can infer from this visualization. For latitude between -47.7 and -48.8 there are many houses , which would mean that maybe it’s an ideal location isn’t it ? But when we talk about longitude we can see that concentration is high between -122.2 to -122.4. Which would mean that most of the buy’s has been for this particular location.

How common factors are affecting the price of the houses ?

We saw the common locations and now we’re going to see few common factors affecting the prices of the house and if so ? then by how much ?

Let us start with , If price is getting affecting by living area of the house or not ?

plt.scatter(data.price,data.sqft_living) plt.title("Price vs Square Feet")

plt.scatter(data.price,data.long) plt.title("Price vs Location of the area")

The plot that we used above is called scatter plot , scatter plot helps us to see how our data points are scattered and are usually used for two variables. From the first figure we can see that more the living area , more the price though data is concentrated towards a particular price zone , but from the figure we can see that the data points seem to be in linear direction. Thanks to scatter plot we can also see some irregularities that the house with the highest square feet was sold for very less , maybe there is another factor or probably the data must be wrong. The second figure tells us about the location of the houses in terms of longitude and it gives us quite an interesting observation that -122.2 to -122.4 sells houses at much higher amount.

plt.scatter(data.price,data.lat) plt.xlabel("Price") plt.ylabel('Latitude') plt.title("Latitude vs Price")

plt.scatter(data.bedrooms,data.price) plt.title("Bedroom and Price ") plt.xlabel("Bedrooms") plt.ylabel("Price") plt.show() sns.despine

We can see more factors affecting the price

plt.scatter((data['sqft_living']+data['sqft_basement']),data['price'])

plt.scatter(data.waterfront,data.price) plt.title("Waterfront vs Price ( 0= no waterfront)")

train1 = data.drop(['id', 'price'],axis=1)

data.floors.value_counts().plot(kind='bar')

plt.scatter(data.floors,data.price)

plt.scatter(data.condition,data.price)

plt.scatter(data.zipcode,data.price) plt.title("Which is the pricey location by zipcode?")

As we can see from all the above representation that many factors are affecting the prices of the house , like square feet which increases the price of the house and even location influencing the prices of the house.

Now that we are familiar with all these representation and can tell our own story let us move and create a model to which would predict the price of the house based upon the other factors such as square feet , water front etc . We are going to see what is linear regression and how do we do it ?

Linear Regression :

In easy words a model in statistics which helps us predicts the future based upon past relationship of variables. So when you see your scatter plot being having data points placed linearly you know regression can help you!

Regression works on the line equation , y=mx+c , trend line is set through the data points to predict the outcome.