Merge pull request #1535 from Archi20876/main

Add readme file on the project files under the directory 'Classification models'.

Classification Models/Bird species classification/README.md

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+#  **Bird Species Classification** 🐦
+
+### 🎯 Goal
+The primary goal of this project is to build deep learning models to classify Bird species .
+
+### 🧵 Dataset : https://www.kaggle.com/datasets/akash2907/bird-species-classification/data
+
+### 🧾 Description
+This dataset consists of over 170 labeled images of birds, including validation images. Each image belongs to only one bird category. The challenge is to develop models that can accurately classify these images into the correct species.
+
+### 📚 Libraries Needed
+- os - Provides functions to interact with the operating system.
+- shutil - Offers file operations like copying, moving, and removing files.
+- time - Used for time-related functions.
+- torch - Core library for PyTorch, used for deep learning.
+- torch.nn - Contains neural network layers and loss functions.
+- torchvision - Provides datasets, models, and image transformation tools for computer vision.
+- torchvision.transforms - Contains common image transformation operations.
+- torch.optim - Optimizers for training neural networks.
+- matplotlib.pyplot - Used for data visualization, like plotting graphs.
+
+## EDA Result 👉 [Classified Bird Species](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Bird%20species%20classification/bird-species-classification.ipynb)
+
+
+

Classification Models/CSGO Round Winner Classification/README.md

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+#  **CSGO Round Winter Classification** ❄️
+
+### 🎯 Goal
+Predict the Winning individual of Snapshots round
+
+### 🧵 Dataset :  [LINK](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/CSGO%20Round%20Winner%20Classification/Dataset/csgo.csv)
+
+
+### 🧾 Description
+This dataset consists of over 170 labeled images of birds, including validation images. Each image belongs to only one bird category. The challenge is to develop models that can accurately classify these images into the correct species.
+
+### 📚 Libraries Needed
+- Numpy
+- Pandas
+- Matplotlib
+- Seaborn
+- Scikit-Learn
+
+## EDA Result 👉 [CSGO Round Winter.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/CSGO%20Round%20Winner%20Classification/Model/CS_GO_Round_Winner_Classification.ipynb)
+### For more information please refer to [Model](https://github.com/Archi20876/machine-learning-repos/tree/main/Classification%20Models/CSGO%20Round%20Winner%20Classification/Model)

Classification Models/Cartier Jewelry Classification/README.md

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+#  **Cartier Jewelry Classification** 💎
+
+### 🎯 Goal
+The aim of this project is to make a classification model, which will classify the jewelries based on the various features.
+
+
+### 🧵 Dataset :  [LINK](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Cartier%20Jewelry%20Classification/Dataset/cartier_catalog.csv)
+
+### 🧾 Description
+Cartier International SNC, or simply Cartier (/ˈkɑːrtieɪ/; French: [kaʁtje]), is a French luxury goods conglomerate which designs, manufactures, distributes, and sells jewellery and watches. Founded by Louis-François Cartier in Paris in 1847, the company remained under family control until 1964. The company maintains its headquarters in Paris, although it is a wholly owned subsidiary of the Swiss Richemont Group. Cartier operates more than 200 stores in 125 countries, with three Temples (Historical Maisons) in London, New York, and Paris.
+
+### 📚 Libraries Needed
+- Numpy
+- Pandas
+- Matplotlib
+- XgBoost
+- Sklearn
+- seaborn
+
+
+## EDA Result 👉 [  Cartier Classification.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Cartier%20Jewelry%20Classification/Model/cartier_jewelry_classification.ipynb)
+### For more information please refer to [Model](https://github.com/Archi20876/machine-learning-repos/tree/main/Classification%20Models/Cartier%20Jewelry%20Classification/Model)

Classification Models/Mental Disorder Classification/README.md

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+# **Mental Disorder Classification** ❤️
+
+### 🎯 Goal
+To develop and validate a machine learning model that accurately classifies different types of mental disorders based on clinical and demographic data.
+
+
+### 🧵 Dataset :  [LINK](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Mental%20Disorder%20Classification/Dataset-Mental-Disorders.csv)
+
+### 🧾 Description
+A mental disorder classification system is designed to identify, categorize, and diagnose various mental health conditions based on specific criteria. This classification helps in understanding the nature, symptoms, and causes of mental disorders, which include a wide range of conditions such as anxiety disorders, mood disorders, psychotic disorders, eating disorders, and neurodevelopmental disorders.
+
+### 📚 Libraries Needed
+- Numpy
+- Pandas
+- Matplotlib
+
+
+## EDA Result 👉 [  Mental Disorder Classification.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Mental%20Disorder%20Classification/mental-disorder-classification.ipynb)

Classification Models/Mushroom Classification Model/README.md

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+# **Mushroom Classificatfion Model** 🍄
+
+### 🎯 Goal
+To develop and evaluate a machine learning model that accurately classifies mushrooms as edible or poisonous based on their physical characteristics.
+
+### 🧵 Dataset :  [LINK](https://1drv.ms/x/c/64CA0463A0426356/EaArtcNH2YVAnhGowl_QgSoBJ1lD4gWxfxO4fyKMYY7gDw?e=dppRej)
+
+### 🧾 Description
+A mushroom classification model is a machine learning system designed to identify and categorize mushrooms based on their physical characteristics. The model aims to determine whether a mushroom is edible or poisonous by analyzing features such as cap shape, color, gill size, stem texture, and spore print color.
+
+### 📚 Libraries Needed
+- numpy
+- pandas
+- seaborn
+- matplotlib.pyplot
+- sklearn.model_selection.train_test_split
+- sklearn.preprocessing.OneHotEncoder
+- sklearn.preprocessing.LabelEncoder
+- sklearn.neighbors.KNeighborsClassifier
+- sklearn.ensemble.RandomForestClassifier
+- sklearn.metrics.accuracy_score
+- sklearn.metrics.classification_report
+- sklearn.metrics.confusion_matrix
+
+## EDA Result 👉 [  Mushroom Classification.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Mushroom%20Classification%20Model/Mushroom%20Classification%20Model.ipynb)
 

Classification Models/SONAR Classifier/Readme.md

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+# **Sonar Classifier** 📡
+
+### 🎯 Goal
+To develop and evaluate a machine learning model for classifying sonar signals, distinguishing between objects such as mines and rocks based on sonar data.
+
+### 🧵 Dataset :  [LINK](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/SONAR%20Classifier/sonar_data1.csv)
+
+### 🧾 Description
+Sonar (Sound Navigation and Ranging) is a technology that uses sound waves to detect, locate, and identify objects underwater. It works by emitting sound pulses (or "pings") and listening for echoes that bounce back from objects. The time it takes for the echoes to return helps determine the distance, size, and location of the object.
+
+### 📚 Libraries Needed
+- pandas
+- numpy
+- sklearn
+- Logistics Regression
+
+## EDA Result 👉 [  Sonar Classifier.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/SONAR%20Classifier/Sonar_Classifier%20.ipynb)

Classification Models/Spatial clustering and hot spot analysis/README.md

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+# **Spatial Clustering and Hot Spot Analysis** 🌏
+
+### 🎯 Goal
+
+"To identify spatial clusters and statistically significant hot spots in geographical data, enabling the detection of patterns, trends, and anomalies for informed decision-making.
+
+### 🧵 Dataset :  [LINK](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Spatial%20clustering%20and%20hot%20spot%20analysis/synthetic_spatial_data.csv)
+
+
+### 🧾 Description
+Spatial Cluster and Hot Spot Analysis are techniques used in geographic information systems (GIS) and spatial data analysis to identify patterns, concentrations, or anomalies in spatial data.
+
+### 📚 Libraries Needed
+- pandas
+- numpy
+- geopandas
+- shapely.geometry.Point
+- sklearn.cluster.DBSCAN
+- esda.getisord.G
+- libpysal.weights.DistanceBand
+- matplotlib.pyplot
+
+
+## EDA Result 👉 [  Spatial Cluster and Hot Spot Analysis.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Spatial%20clustering%20and%20hot%20spot%20analysis/Spatial_clustering_and_hot_spot_analysis_.ipynb)

Classification Models/Tomato Leaf Diseases Classification/Readme.md

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+# **Tomato Leaf Diseases Classification** 🍅
+
+### 🎯 Goal
+
+The aim of this project is to make a classification model, which will classify the leaf of the tomatoes based on its diseases , so that farmers can find cure to thier diseases.
+
+
+### 🧵 Dataset :  [LINK](https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf)
+
+
+### 🧾 Description
+Tomato leaf disease refers to a variety of conditions that affect the leaves of tomato plants, often leading to reduced yield and quality. These diseases can be caused by pathogens such as fungi, bacteria, and viruses, as well as environmental factors.  
+
+### 📚 Libraries Needed
+- os
+- tensorflow
+- matplotlib.pyplot
+- matplotlib.image
+- tensorflow.keras.models
+- tensorflow.keras.optimizers
+- tensorflow.keras.callbacks
+- tensorflow.keras.regularizers
+- tensorflow.keras.preprocessing.image
+- tensorflow.keras.layers
+- tensorflow.keras.applications
+
+
+
+## EDA Result 👉 [  Tomato Leaf Classification.ipynb](https://github.com/Archi20876/machine-learning-repos/blob/main/Classification%20Models/Tomato%20Leaf%20Diseases%20Classification/Tomato_Leaf_Diseases_Classification.ipynb)