INSTALLATION PROCESS:
Numpy Installation: pip install numpy
Scipy Installation: pip install scipy
Jupyter Installation: pip install jupyter
Statsmodels installation: pip install statsmodels
Pandas installation: pipip
piio
PROGRAM & OUTPUT:
PROGRAM & OUTPUT:
Creating a data frame using List:
Creating Data frame from dict of ndarray/lists:
Dealing with Rows and Columns:
PROGRAM:
OUTPUT:
IRIS DATA SET:
ALGORITHM:
Step 1: Download the IRIS dataset from the Kaggle website and save in
Documents or any other folder do you want.
Link: https://www.kaggle.com/code/bharath25/descriptive-statistics-and-
machine-learning-iris/data
iris. head (10)
iris. Shape
iris.info ()
iris. describe ()
iris.isnull ().sum ()
iris.value_counts (“Species”)
ALGORITHM:
Step 1: Download the Pima Indians Diabetes dataset
Link: https://www.kaggle.com/datasets/uciml/pima-indians-diabetes-database?
resource=download
PROGRAM:
a) Univariate analysis: Frequency, Mean, Median, Mode, Variance,
Standard Deviation, Skewness and Kurtosis.
print (df.shape)
print (df.info ())
Print (df.mean ())
Print (df.median ())
Print (df.mode ())
Print (df.std ())
Print (df.var ())
Print (df.skew ())
Print (df.kurtosis ())
Df.describe ()
5 b) Bivariate Analysis: Linear and Logistic Regression Modeling.
LOGISTIC REGRESSION:
5 c) MULTIPLE REGRESSION ANALYSIS.
ALGORITHM:
Step 1: Import Libraries.
Step 2: Import dataset.
Step 3: Define x and y.
Step 4: Train the model on the training set.
Step 5: Predict the test set results.
Step 6: Evaluate the model.
Step 7: Plot the results.
ALGORITHM:
Step 1: Download Heart dataset from kaggle.
Link: https://www.kaggle.com/datasets/zhaoyingzhu/heartcsv
Step 2: Save that in downloads or any other Folder and install packages.
.PROGRAM:
BOX PLOT:
a) Normal Curve:
b) Density Plots:
c) Correlation and Scatter plots:
Correlation plot
Scatter plot
d) Histogram:
e) Three Dimensional Plotting:
RESULT:
Thus the program was executed successfully.
AIM:
To create an insight Geographic Data with Basemap.
ALGORITHM:
Step 1: Install Basemap. The zip file occurs extract the original file.
Step 2: import Packages.
Step3: Save that in downloads or any other Folder.
Step 4: Apply these following commands.
Step 5: The Output will display.
PROGRAM & OUTPUT:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
plt.figure(figsize=(8, 8))
m = Basemap(projection='ortho', resolution=None, lat_0=50, lon_0=-100)
m.bluemarble(scale=0.5);
fig = plt.figure(figsize=(8, 8))
m = Basemap(projection='lcc', resolution=None,
width=8E6, height=8E6,
lat_0=45, lon_0=-100,)
m.etopo(scale=0.5, alpha=0.5)
# Map (long, lat) to (x, y) for plotting
x, y = m(-122.3, 47.6)
plt.plot(x, y, 'ok', markersize=5)
plt.text(x, y, ' Seattle', fontsize=12);
RESULT:
Thus the program was executed successfully.
REFERENCES:
1. Kaggle.com
2. UCI.
3. PIMA Indian Diabetes Data Set.
