In this repository, you will find the real state price prediction using numpy, sci-kit learn, pandas, matplotlib, etc and at the end you will be able to train data, familier with data visualization with the help of matplotlib. You will be able to use pandas to explore the data and sci-kit learn to train and predict the price of housing with various models.

Dragon Real State

Day 1

Prediction

import pandas as pd
from sklearn.tree import DecisionTreeClassifier

music_data = pd.read_csv("music.csv")  # import our dataset
X = music_data.drop(columns=['genre']) #input  set
y = music_data['genre']                #output set

model= DecisionTreeClassifier()         #create a model
model.fit(X,y)                          # train the model
predictions = model.predict([ [32,1], [22,0]])
predictions

Day 2

Today I learn something about the csv files that gifted a huge amount of errors and tackling and learning about those errors were interesting to learn. Basically I encountered with "errno2 error" which says they didn't find the mentioned directory and at the end trying to shift the location of "weather_data", the issue was solved at an instant. I algo got to familier with the installation of pip (Preferred Installer Program). I also learn about the way how we can get particular section or convert the csv file to Dictionary.

coding section

import pandas as pd

weather_data = pd.read_csv("weather_data.csv")
# print(type(weather_data))                   
print(weather_data)

data_dict = weather_data.to_dict()
# print(data_dict)
temp_list = weather_data['temp'].to_dict()
# print(temp_list)

print(f"The no of list is {len(temp_list)}")
# average_temp = sum(weather_data["temp"] / len(temp_list))                 # traditional way to find mean
# print(f"The total sum of the temperature is {sum(weather_data["temp"])}")
# print(f"The average temperature of the weekend is {average_temp}")

print(weather_data["temp"].mean())                                         # modern way to find an average 

Day 3

Today, I jumped into the famous project of machine learning project "Dragon Real State" and worked in jupyter notebook. I learned the basic concept of matplotib and pandas.

read_excel(".xlsx") function

This particular function is used to import the excel file where read_csv(".csv") only worked where the data were saved in csv format.

data = read_excel(".xlsx)

.head()

.head() function is used to display the first five columns and all rows of the csv or excel data and it mainly check wheather the data is working or not.

data.head()

.describe()

.describe() function is used to describe the nature of the data and displays the statistic values like count, mean, standard deviation, percentile, mean, median etc

head.describe()

.info()

.info() displays the count of the data of each rows to confirm if there is missing data or space inbetween the cells.

head.info()

.value_counts()

In this function, it displays the number of 0 and 1 in the certain row

syntax:

data['CRIM'].value_counts()

%matplotlib inline

The %matplotlib inline command tells the IPython environment to draw the plots immediately after the current cell.

import matplotlib.pyplot as plt and housing.hist(bins= 50, figsize =(20,15)) are used to import the pyplot of matplotlib and plot a histogram

The output histogram is given below:

Day 3

It is always interesting and entertaining to learn new things and today I learned about the used of pandas and its methods to plot the statistical data. The main methods used are plot and scatter plot to study the correlation of the particular data. I also learned to scatter the individual plot interducing their coordinate values.

corr_matrix['MEDV'].sort_values(ascending = True) 

The involved program is given below

rom pandas.plotting import scatter_matrix
attributes = ['MEDV','RM', 'ZN', 'LSTAT']
scatter_matrix(housing[attributes], figsize = (12,8))

find code file for other