adding old .py code

Desktop Application/Advanced/Java/Notification REST API/target/classes/application.properties

@@ -0,0 +1 @@
+

Desktop Application/Advanced/Java/Red Black Tree GUI/.project

@@ -14,4 +14,15 @@
 	<natures>
 		<nature>org.eclipse.jdt.core.javanature</nature>
 	</natures>
+	<filteredResources>
+		<filter>
+			<id>1732552145514</id>
+			<name></name>
+			<type>30</type>
+			<matcher>
+				<id>org.eclipse.core.resources.regexFilterMatcher</id>
+				<arguments>node_modules|\.git|__CREATED_BY_JAVA_LANGUAGE_SERVER__</arguments>
+			</matcher>
+		</filter>
+	</filteredResources>
 </projectDescription>

Desktop Application/Basic/Java/Temperature Convertor Tool/target/classes/META-INF/MANIFEST.MF

@@ -0,0 +1,3 @@
+Manifest-Version: 1.0
+Main-Class: com.example.main.HelloApplication
+

Desktop Application/Intermediate/Java/CMD/.project

@@ -14,4 +14,15 @@
 	<natures>
 		<nature>org.eclipse.jdt.core.javanature</nature>
 	</natures>
+	<filteredResources>
+		<filter>
+			<id>1732552145093</id>
+			<name></name>
+			<type>30</type>
+			<matcher>
+				<id>org.eclipse.core.resources.regexFilterMatcher</id>
+				<arguments>node_modules|\.git|__CREATED_BY_JAVA_LANGUAGE_SERVER__</arguments>
+			</matcher>
+		</filter>
+	</filteredResources>
 </projectDescription>

Desktop Application/Intermediate/Java/Mail API/target/classes/application.properties

@@ -0,0 +1 @@
+

Desktop Application/Intermediate/Java/Router Synchronization/.project

@@ -14,4 +14,15 @@
 	<natures>
 		<nature>org.eclipse.jdt.core.javanature</nature>
 	</natures>
+	<filteredResources>
+		<filter>
+			<id>1732552145095</id>
+			<name></name>
+			<type>30</type>
+			<matcher>
+				<id>org.eclipse.core.resources.regexFilterMatcher</id>
+				<arguments>node_modules|\.git|__CREATED_BY_JAVA_LANGUAGE_SERVER__</arguments>
+			</matcher>
+		</filter>
+	</filteredResources>
 </projectDescription>

Machine Learning and Data Science/Basic/Movie Recommendation System/MovieRecSystem.py

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+
+import pandas as pd
+import numpy as np
+import difflib
+from sklearn.feature_extraction.text import TfidfVectorizer 
+from sklearn.metrics.pairwise import cosine_similarity
+
+"""feature extracion - #converting text to numbers
+cosine similarity - #checking for similarities
+"""
+
+movies_dataset=pd.read_csv('F:\OpenSource\Project-Guidance\Machine Learning and Data Science\Basic\Movie Recommendation System\movies.csv')
+movies_dataset.head()
+
+movies_dataset.shape
+
+#feature selection
+selected_features=['genres', 'keywords', 'tagline', 'cast', 'director']
+print(selected_features)
+
+#replacing null values with null string
+#replacing null values with string will help in easy understandi
+for feature in selected_features:
+  movies_dataset[feature]=movies_dataset[feature].fillna('')
+
+#combining the 5 features
+combined_parameters=movies_dataset['genres']+' '+movies_dataset['keywords']+' '+movies_dataset['tagline']+' '+movies_dataset['cast']+' '+movies_dataset['director']
+print(combined_parameters)
+
+#converting text to feature vectors
+#models can understand only vectors(numbers)
+# so it is necessary to featurize the text data to numerical data
+#Tfid gives a numerical statistic to indicate how important a word is to 
+# a document with respect to a collection of documents
+#It is a weighting factor
+#It also tells how often a word occurs/ or words that appear important and are common
+vectorizer=TfidfVectorizer()
+feature_vectors=vectorizer.fit_transform(combined_parameters)
+print(feature_vectors)
+
+#using cosine similarity function(compares with other values and recommends)
+# Text Similarity has to determine how the two text documents close to each other in terms of their context or meaning.
+# Cosine similarity is one of the metric to measure the text-similarity between two documents irrespective of their size in Natural language Processing. 
+# A word is represented into a vector form. The text documents are represented in n-dimensional vector space.
+
+# Mathematically, Cosine similarity metric measures the cosine of the angle between two n-dimensional vectors projected in a multi-dimensional space. 
+# The Cosine similarity of two documents will range from 0 to 1. If the Cosine similarity score is 1, it means two vectors have the same orientation.
+#  The value closer to 0 indicates that the two documents have less similarity.
+similarity=cosine_similarity(feature_vectors)
+print(similarity)
+
+"""Recommendation System"""
+
+#getting input from the user
+movie_name=input("Enter your favorite movie")
+
+list_of_alltitles=movies_dataset['title'].tolist()
+print(list_of_alltitles)
+
+#finding the close match - only 1
+# get_close_matches(word, possibilities, n, cutoff) accepts four parameters:
+# word - the word to find close matches for in our list
+# possibilities - the list in which to search for close matches of word
+# n (optional) - the maximum number of close matches to return. Must be > 0. Default is 3.
+# cutoff (optional) - a float in the range [0, 1] that a possibility must score in order
+#  to be considered similar to word.
+#  0 is very lenient, 1 is very strict. Default is 0.6.
+find_close_match=difflib.get_close_matches(movie_name,list_of_alltitles,1)
+close_match=find_close_match[0]
+print(close_match)
+
+#find the index of the movie
+index_of_the_movie=movies_dataset[movies_dataset.title==close_match]['index'].values[0]
+print(index_of_the_movie)
+
+similar_movies=list(enumerate(similarity[index_of_the_movie]))
+print(similar_movies)
+
+#sort based on similarity confidence
+sorted_similar_movies=sorted(similar_movies,key=lambda x:x[1],reverse=True)
+print(sorted_similar_movies)
+
+print("Movies suggested:")
+i=1
+for movie in sorted_similar_movies:
+  ind=movie[0]
+  title_from_index=movies_dataset[movies_dataset.index==ind]['title'].values[0]
+  if(i<=30):
+    print(i,'.',title_from_index)
+    i+=1

Mobile Applications/Advanced/Chat Application/android/gradle/wrapper/gradle-wrapper.properties

@@ -1,6 +1,6 @@
-#Fri Jun 23 08:50:38 CEST 2017
+#Mon Nov 25 21:59:05 IST 2024
 distributionBase=GRADLE_USER_HOME
+distributionUrl=https\://services.gradle.org/distributions/gradle-8.9-bin.zip
 distributionPath=wrapper/dists
-zipStoreBase=GRADLE_USER_HOME
 zipStorePath=wrapper/dists
-distributionUrl=https\://services.gradle.org/distributions/gradle-6.7-all.zip
+zipStoreBase=GRADLE_USER_HOME