ANALYSTDHIREN

In this project i have created a  Multiple Page Inter-connected Business Intelligence (BI) Dashboard using Power BI. I have analyzed the data to forecast the crypto currecy using forecast mode of PowerBI Data link : https://docs.google.com/spreadsheets/d/1Lc380wCA1DkS2ejvbM8GtL2w_teZ80BM/edit?usp=sharing&ouid=109399052699278618880&rtpof=true&sd=true 1: Started by Using Slicer pane from Power BI, for sorting all the cryptop scripts,  Below is the output  2: Used Slicer pane to sort the data by Year ( Again we had the data in form of full dates, so we carried out "Data transformation" and thus sorted Years out of the date seperatly in new coloum. Then we implimented slicer on year column the below result was obtained. 3:   Adding Cards  : Using cards pane we have added three different values. Here we have added cards with High, Low, Average.                           ...

 Created  Dashboard using different types of charts including a butterfly chart, a donut chart, a scatter plot Here we do the customer analysis - Descriptive type I have carried out the below task to get the final dashboard output Revenue per state Revenue-based on the month of the year. Revenue-based on Age Quantity - Discount % Correlation Percentage of Revenue per region Revenue per category per region Data set link: Click Here<< 1: Revenue per State  2: Revenue per Month 3: Revenue Based on Age 4:  Quantity - Discount % Correlation 5:  Percentage of Revenue per region 6:  Revenue per category per region Now making a Dashboard based on the above-achieved graphs  

In this project, I did Sales Conversion Optimization by analyzing  Facebook  ad campaigns Prediction using Random Forest Regressor Aim: To optimize Sales conversion and predict future sales. Approach:  Exploratory Data Analysis using matplotlib and seaborn and model training using  Random Forest Regressor. Importing Libraries import numpy as np import pandas as pd import os df=pd.read_csv("File/path.csv") df.head() df.info()   #Checking for null values  # Doing Exploratory Data Analysis df.shape (1143, 11) df . describe () import matplotlib.pyplot as plt import seaborn as sns Correlation Matrix : import matplotlib.pyplot as plt import seaborn as sns g=sns.heatmap(df[["Impressions","Clicks","Spent","Total_Conversion", "Approved_Conversion"]].corr(),annot=True ,fmt=".2f", cmap="coolwarm") # Here above it's clear, "Impressions" and "Total_Conversion" are more correlated...