diff --git a/intern-basics/Bank Deposit Prediction/Bank_Marketing_Data_Description.pdf b/intern-basics/Bank Deposit Prediction/Bank_Marketing_Data_Description.pdf
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index 0000000..195d3fc
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diff --git a/intern-basics/Bank Deposit Prediction/bank_marketing.csv b/intern-basics/Bank Deposit Prediction/bank_marketing.csv
new file mode 100644
index 0000000..89c8c27
--- /dev/null
+++ b/intern-basics/Bank Deposit Prediction/bank_marketing.csv	
@@ -0,0 +1,5582 @@
+,age,job,marital,education,default,balance,housing,loan,contact,day,month,duration,campaign,pdays,previous,poutcome,deposit
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diff --git a/intern-basics/Bank Deposit Prediction/bank_marketing.py b/intern-basics/Bank Deposit Prediction/bank_marketing.py
new file mode 100644
index 0000000..3ef6d79
--- /dev/null
+++ b/intern-basics/Bank Deposit Prediction/bank_marketing.py	
@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+"""bank_marketing.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Ae4yjRS63UxULwOB8VwkvZWSWdM6piKm
+"""
+
+from google.colab import files
+a= files.upload()
+
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import seaborn as sns
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+
+data = pd.read_csv("bank_marketing.csv")
+data.head()
+
+columns = []
+for i in data.columns:
+  columns.append(i)
+columns
+
+for i in columns:
+  print("number of unique values is",len(np.unique(data[i])))
+
+data.shape
+
+numerical_column = ['Unnamed: 0','age','balance','day','duration','campaign','pdays','previous']
+categorical_column = []
+for i in columns:
+  if i not in numerical_column:
+    categorical_column.append(i)
+categorical_column
+
+le = LabelEncoder()
+for i in categorical_column:
+  data[i] = le.fit_transform(data[i])
+
+sns.countplot(data['deposit'])
+
+x = data.iloc[:,:17].values
+y = data.iloc[:,17].values
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
+
+cls = RandomForestClassifier()
+cls.fit(x_train,y_train)
+
+y_pred = cls.predict(x_test)
+
+import matplotlib.pyplot as plt
+
+sns.countplot(data['deposit'],hue=data['job'])
+
+metrics.f1_score(y_test,y_pred)
+
+
+
+
+
diff --git a/job recommendation system/Readme.md b/job recommendation system/Readme.md
new file mode 100644
index 0000000..690a8f1
--- /dev/null
+++ b/job recommendation system/Readme.md	
@@ -0,0 +1,41 @@
+# job Recommendation system
+## What is Recommendation system?
+
+*Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code. By George Seif, AI / Machine Learning Engineer*
+
+## Types of Recommendation system
+**There are three basic, commonly used, types of recommender systems**
+* collaborative filtering
+* content-based filtering
+* hybrid recommendation systems.
+
+## For this project we are going  to use Content based Filtering system.
+## What is content based filtering?
+
+*The point of content-based filtering system is to know the content of both user and item. Usually it constructs and then compare user-profile and item-profile using the content of shared attribute space*
+
+<img src = "https://image.slidesharecdn.com/crabintroduction-111002094330-phpapp02/95/crab-a-python-framework-for-building-recommender-systems-42-728.jpg?cb=1317548872"></img>
+
+# WorkFlow
+
+# User side data
+
+| id | Name | Skills | Applied job | Experience |
+| ----------- | ----------- | ----- | ----------- | ----------- |
+| 1 | swayam |python | software developer| 2 | 
+| 2 | ashish | java| Android developer| 0 | 
+
+
+# Company side data
+
+| company name | required post | minimum experience |
+| -------------- | --------- | ------------ |
+| x | software developer | 1 |
+
+
+*as we know Content filtering works by matching strings of characters. When the strings match, the content is not allowed through. Content filters are often part of Internet firewalls. In such a usage content filtering is serving a security purpose, but content filtering is also used to implement company policies related to information system usage*
+
+* it will predict according to the data given by the user
+* it gets trained on the user data
+* so if a user is applying for specific post in a company then it will recommend all the similar posts of different companies
+* simmilarly for companies it will suggest them the user id applying for the soimilar kind of post